CN102930155B - Obtain the method and device of the early-warning parameters of electricity needs - Google Patents

Abstract

The invention discloses a method and a device for acquiring early warning parameters of power demand. Among them, the method includes: obtaining the data sequence used to generate the early warning index; filtering the data sequence according to the adjustment parameters to obtain the data sequence; calculating the trend index of the data sequence containing the trend item and the period item, and obtaining the early warning index sequence; extracting The benchmark index and the selected index in the early warning index sequence; the correlation calculation is performed according to the time difference analysis model and/or the K-L information model to obtain the correlation coefficient between the selected index and the benchmark index, and according to the correlation coefficient Screen the selected indicators to obtain leading indicators and consistent indicators; synthesize the leading indicators and consistent indicators to obtain the leading composite index and consistent composite index as early warning parameters. Through the present invention, it is possible to accurately acquire the early warning parameters of electric power demand, thereby accurately formulating reasonable and scientific countermeasures according to short-term periodic fluctuations.

Description

Method and device for obtaining early warning parameters of power demand

technical field

The present invention relates to the field of electric power, in particular, to a method and device for obtaining early warning parameters of electric power demand.

Background technique

The cyclical fluctuation of the economy is an objective phenomenon in economic and social development. It is an objective law that does not depend on human will in the process of economic growth. It is unrealistic to try to forcefully eliminate cyclical fluctuations through various artificial means. The artificially enforced elimination of periodic volatility can also exacerbate volatility. Through the study of the operating law of the economic cycle, we can grasp the fluctuation law of the economic cycle, and then take appropriate measures to reduce the amplitude of the economic cycle fluctuation and prolong the economic fluctuation cycle, so as to achieve the goal of sustainable economic growth.

As a core issue of macroeconomic research, the study of economic cycle has always been valued by the governments of various countries and many economists. With the development of economic theory and the progress of econometric technology, many scholars at home and abroad have begun to use quantitative methods to monitor and predict the economic cycle. The strength of expansion and contraction, etc., so as to formulate scientific and reasonable countermeasures for the government and enterprises according to the characteristics and formation mechanisms of different cycles, slow down the magnitude of cyclical fluctuations, and reduce the damage caused by cyclical fluctuations to economic development.

Economic activities are the driving force of electricity demand, so electricity demand will also fluctuate periodically, but in the field of electricity demand, the means of analyzing periodic fluctuations in electricity demand is to compare the economic growth curve and electricity consumption growth curve for many years, combined with economics According to the classification of my country's economic development stages, experts subjectively judge that the fluctuation cycle of power demand is 9-11 years, and there is no method for studying the periodic fluctuation of short-term power demand.

In order to solve the above problems, forecasting technology can be used to directly predict the future growth rate of electricity demand and analyze its fluctuations. However, forecasting techniques have many flaws:

1. The basic data used has not been adjusted seasonally. The Spring Festival, the number of weekends in a month, the number of holidays, and leap years all have a great impact on the basic data. The fluctuation trend predicted based on this will be distorted;

2. Regardless of using causal relationship models such as regression, per capita electricity consumption, and neural networks, or using time series models such as ARIMA and logistic, reasonable extrapolation is done based on the historical trend of statistical data, which is equivalent to focusing on Figure 1 According to the changing law of medium and long-term trends, it is not possible to quickly make reasonable countermeasures to compound the current economy according to the changing law of long-term trends.

At present, in the field of power demand, forecasting technology is used to obtain short-term periodic fluctuation distortion, and early warning parameters that meet power demand cannot be obtained, resulting in the inability to formulate reasonable measures to deal with periodic fluctuations. No effective solution has been proposed yet. Program.

Contents of the invention

Aiming at the problem in related technologies that using forecasting technology in the field of power demand to obtain short-term periodic fluctuation distortion, it is impossible to obtain early warning parameters that meet power demand, resulting in the inability to formulate reasonable measures to deal with periodic fluctuations. No effective solution has yet been proposed. Therefore, the main purpose of the present invention is to provide a method and device for obtaining early warning parameters of power demand, so as to solve the above problems.

In order to achieve the above object, according to one aspect of the present invention, a method for obtaining early warning parameters of power demand is provided, the method includes: obtaining a data sequence for generating early warning indicators; screening the data sequence according to the adjustment parameters to obtain Data series containing trend items and period items; calculate the trend index of the data series containing trend items and period items, and filter the data series containing trend items and period items according to the trend index to obtain the early warning index sequence, The early warning index sequence is the data whose trend index is increasing in the data sequence; the power generation in the early warning index sequence is extracted as the benchmark index, and the indicators other than the power generation are extracted as the selected indicators; according to the time difference analysis model and/or the K-L information volume model Carry out correlation calculation on the early warning indicators to obtain the correlation coefficient between each selected index and the benchmark index, and screen the selected indicators according to the correlation coefficient to obtain leading indicators and consistent indicators; according to the synthetic index model, the The leading index and the consistent index are synthesized to obtain the leading synthetic index and the consistent synthetic index as early warning parameters.

Further, use the time difference analysis model and/or the KL information model to perform correlation calculations on early warning indicators, and use the correlation coefficient between each selected index and the benchmark index, and filter the selected indicators according to the correlation coefficient, The steps for obtaining the leading index and the consistent index include: obtaining the correlation coefficient _r l between each selected index and the benchmark index according to the following formula:

${\mathrm{<span\; class="notranslate">\; r</span>}}_{\mathrm{<span\; class="notranslate">\; l</span>}}<span\; class="notranslate">\; =</span>\frac{\underset{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{{\mathrm{<span\; class="notranslate">\; no</span>}}_{\mathrm{<span\; class="notranslate">\; l</span>}}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; l</span>}}<span\; class="notranslate">\; -</span>\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{\mathrm{<span\; class="notranslate">\; x</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; -</span>\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{\mathrm{<span\; class="notranslate">\; the\; y</span>}}<span\; class="notranslate">\; )</span>}{\sqrt{\underset{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{{\mathrm{<span\; class="notranslate">\; no</span>}}_{\mathrm{<span\; class="notranslate">\; l</span>}}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; l</span>}}<span\; class="notranslate">\; -</span>\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{\mathrm{<span\; class="notranslate">\; x</span>}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; -</span>\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{\mathrm{<span\; class="notranslate">\; the\; y</span>}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}}<span\; class="notranslate">\; ,</span>$

Among them, l=0,±1,±2,…,±12, Y=(y ₁ ,y ₂ ,…,y _n ) is the benchmark index, X=(x ₁ ,x ₂ ,…,x _n ) is Selected indicators, l is the time difference, n _l is the number of all indicators, t=1, 2, ..., n is the number of months; the value of the time difference is within the first value range and the correlation coefficient r _l is greater than the first The selected index of the threshold is used as the leading index, and the selected index whose time difference value is within the second value range and the correlation coefficient r _l is greater than the second threshold is used as the consistent index.

Further, according to the time difference analysis model and/or the KL information model, the correlation calculation is performed on the early warning indicators, and the correlation coefficient between each selected index and the benchmark index is used, and the selected indicators are screened according to the correlation coefficient, The steps to obtain the leading index and the consistent index include: obtaining the correlation coefficient r _l between each selected index and the benchmark index according to the following formula:

Among them, l=0,±1,±2,…,±12, Y=(y ₁ ,y ₂ ,…,y _n ) is the benchmark index, X=(x ₁ ,x ₂ ,…,x _n ) is Selected indicators, l is the time difference, n _l is the number of all indicators, t=1,2,...,n is the number of months; the value of the time difference is within the first value range and the correlation coefficient r _l is greater than the first The selected index of the threshold is used as the initial index of the leading index, and the selected index whose time difference value is within the second value range and the correlation coefficient r _l is greater than the second threshold is used as the initial index of the consistent index; for the benchmark index, the leading index The initial index of the index and the initial index of the consistent index are standardized to obtain the standard benchmark index sequence p _t and the sequence q _t of the standard selected index, where the standard selected index includes the standard leading index and the standard consistent index; according to the following formula Obtain the KL information _k between each standard selected index and the standard benchmark index: k _l =∑p _t ln(p _t /q _t+1 ), where l=0,±1,…,±12, t=1, 2,..., n is the number of months, l is the time difference, n _l is the number of all indicators; the standard that the time difference value is within the third value range and the KL information volume k _l is less than the third threshold is taken Select the index as the leading index, and use the selected index whose time difference value is within the fourth value range and whose KL information volume k _l is less than the fourth threshold as the consistent index.

Further, the step of synthesizing the leading index and the consistent index according to the composite index model to obtain the leading composite index and the consistent composite index as early warning parameters includes: respectively performing symmetrical change processing on the leading index and the consistent index to obtain the leading index symmetry The rate of change C _w,i (t) and the symmetrical rate of change C _z,i (t) of the consistent index, wherein, the leading index is processed symmetrically by the following formula to obtain the symmetrical rate of change C _w,i (t) of the leading index :

in, is the i-th (i=1, 2, ..., k _w ) leading index, t=2, 3,..., n, k _w is the number of leading indexes; the consistent index is changed symmetrically by the following formula, with Obtain the symmetrical rate of change C _z,i (t) of the consistent index: in, is the i-th (i=1, 2, ..., k _z ) consistent index, t = 2, 3, ..., n is the number of months, k _z is the number of consistent indicators; the symmetric rate of change of the leading index C _w, The results obtained after standardization and trend adjustment of _i (t) and the consistent index symmetric rate of change C _z,i (t) are combined and calculated to obtain the leading composite index and the consistent composite index.

Further, the results obtained after standardization and trend adjustment of the leading index symmetrical rate of change C _w,i (t) and the consistent index symmetrical rate of change C _z,i (t) are synthetically calculated to obtain the leading synthetic index and the consistent index The steps of synthesizing the index include: obtaining the standardized factors A _w,i and A _z,i by the following formula: t=2,3,...,n; use the standardization factors A _w,i and A _z,i to carry out the symmetrical change rate C _w,i (t) of the leading index and the symmetrical change rate C _z,i (t) of the consistent index respectively Standardized processing to obtain the standardized rate of change S _w,i (t) and S _z,i (t), where, t=2,3,...,n; carry out average change rate processing on the standardized change rates S _w,i (t) and S _z,i (t) to obtain the standardized average change rates V _w (t) and The normalized average rate of change V _z (t) of the consistent index; the synthetic calculation is carried out according to the normalized average rate of change V _w (t) of the leading index and the normalized average rate of change V _z (t) of the consistent index to obtain the leading synthetic index I _w (t) and the consensus composite index I _z (t), where, $I_z\left(t\right)=I_z(t-1)\&times;\frac{200+V_z\left(t\right)}{200-V_z\left(t\right)},$ And I _w (1)=100, I _z (1)=100.

Further, average rate of change processing is performed on the standardized rate of change S _w,i (t) and S _z,i (t) to obtain the normalized average rate of change V _w (t) of the leading index and the standardized average rate of change of the consistent index The step of V _z (t) includes: using the following formula to process the normalized rate of change S _w,i (t) of the leading index and the standardized rate of change S _z,i (t) of the consistent index respectively, to obtain the leading The average rate of change R _w (t) of indicators and the average rate of change R _z (t) of consistent indicators: Among them, λw _,i and λz _,i are the weights of the leading indicator and the i-th indicator of the consistent indicator respectively; the indicator standardization factor F _w is obtained by the following formula: $f_w=\left[\underset{t=2}{\overset{\mathrm{no}}{\mathrm{\&Sigma;}}}\right|R_w\left(t\right)|/(\mathrm{no}-1)]/\left[\underset{t=2}{\overset{\mathrm{no}}{\mathrm{\&Sigma;}}}\right|R_z\left(t\right)|/(\mathrm{no}-1)];$ According to the standardization factor F _w of the index, the normalized average rate of change is processed to obtain the normalized average rate of change V _w (t) of the leading index and the normalized average rate of change V _z (t) of the consistent index, where V _w (t) = R _w (t)/F _w , V _z (t) = R _z (t).

Further, after obtaining the data sequence used to generate the early warning indicator, the method also includes: preprocessing the data in the data sequence, the preprocessing includes: filling missing data processing, correcting noise data processing, data smoothing processing, and data normalization processing.

In order to achieve the above object, according to one aspect of the present invention, a device for obtaining early warning parameters of power demand is provided, the device includes: a first obtaining module, used to obtain a data sequence for generating early warning indicators; a first processing module , used to filter the data series according to the adjustment parameters to obtain the data series containing trend items and period items; the first calculation module is used to calculate the trend index of the data series containing trend items and period items, and according to the trend The index filters the data sequence containing the trend item and the period item to obtain the early warning index sequence. The early warning index sequence is the data whose trend index is increasing in the data sequence; the first extraction module is used to extract the power generation in the early warning index sequence as the benchmark index, and extract the index other than the power generation as the selected index; the second calculation module is used to perform correlation calculation on the early warning index according to the time difference analysis model and/or the K-L information model, so as to obtain each selected index The correlation coefficient between the index and the benchmark index, and the selected index is screened according to the correlation coefficient to obtain the leading index and the consistent index; the second processing module is used to synthesize the leading index and the consistent index according to the synthetic index model, To obtain the leading composite index and consistent composite index as early warning parameters.

Further, the second calculation module includes: a first sub-calculation module, which is used to obtain the correlation coefficient r _l between each selected index and the benchmark index according to the following formula: Among them, l=0,±1,±2,…,±12, Y=(y ₁ ,y ₂ ,…,y _n ) is the benchmark index, X=(x ₁ ,x ₂ ,…,x _n ) is Selected indicators, l is the time difference, n _l is the number of all indicators, t=1, 2,..., n is the number of months; the first sub-processing module is used to set the time difference within the first value range and The selected index whose correlation coefficient r _l is greater than the first threshold is taken as the leading index, and the selected index whose time difference value is within the second value range and whose correlation coefficient r _l is greater than the second threshold is regarded as the consistent index.

Further, the second calculation module includes: a second sub-calculation module, which is used to obtain the correlation coefficient r _l between each selected indicator and the benchmark indicator according to the following formula: Among them, l=0,±1,±2,…,±12, Y=(y ₁ ,y ₂ ,…,y _n ) is the benchmark index, X=(x ₁ ,x ₂ ,…,x _n ) is Selected indicators, l is the time difference, n _l is the number of all indicators, t=1, 2, ..., n is the number of months; the second sub-processing module is used to set the time difference within the first value range and The selected index whose correlation coefficient r _l is greater than the first threshold is used as the initial index of the leading index, and the selected index whose time difference value is within the second value range and the correlation coefficient r _l is greater than the second threshold is used as the consistent index Initial indicators; the third sub-processing module is used to standardize the benchmark indicators, the initial indicators of the leading indicators and the initial indicators of the consistent indicators, so as to obtain the standard benchmark indicator sequence p _t and the standard selected indicator sequence q _t , wherein, Standard selected indicators include standard leading indicators and standard consistent indicators; the third sub-calculation module is used to obtain the KL information _k l between each standard selected indicator and standard benchmark indicator according to the following formula: k _l =∑p _t ln(p _t /q _t+1 ), where, l=0,±1,…,±12, t=1, 2,..., n is the number of months, l is the time difference, n _l is the number of all indicators;

The fourth sub-processing module is used to take the time difference value within the third value range and the standard selected index whose KL information volume k _l is less than the third threshold as the leading index, and set the time difference value within the fourth value range And the selected index whose KL information amount k _l is less than the fourth threshold is taken as the consistent index.

Further, the second processing module includes: a fifth sub-processing module, which is used to perform symmetrical change processing on the leading index and the consistent index respectively, so as to obtain the symmetrical rate of change C _w,i (t) of the leading index and the symmetrical rate of change C of the consistent index _{z, i} (t), the fifth sub-processing module includes: a fourth sub-calculation module, which is used to perform symmetrical change processing on the leading index through the following formula, so as to obtain the symmetrical rate of change C _w,i (t) of the leading index: in, is the i-th (i=1, 2, ..., k _w ) leading indicator, t=2, 3, ..., n is the number of months, and k _w is the number of leading indicators; the fifth sub-calculation module is used to pass The following formula performs symmetrical change processing on the consistent index to obtain the symmetrical change rate C _z,i (t) of the consistent index: in, is the i-th (i=1, 2,...,k _z ) consistent index, t=2,3,...,n, k _z is the number of consistent indexes; the sixth sub-processing module is used to be symmetrical to the leading index The results obtained after standardization and trend adjustment of the rate of change C _w,i (t) and the symmetrical rate of change of the consistent index C _z,i (t) are combined and calculated to obtain the leading composite index and the consistent composite index.

Further, the sixth sub-processing module includes: a sixth sub-calculation module, which is used to obtain the normalization factors A _{w, i} and A _{z, i} through the following formula: t=2,3,...,n; the seventh sub-processing module is used to use normalization factors A _w,i and A _z,i to respectively convert the leading index symmetric rate of change C _w,i (t) and the consistent index symmetric rate of change C _z,i (t) is standardized to obtain the standardized rate of change S _w,i (t) and S _z,i (t), where, t=2,3,...,n;

The eighth sub-processing module is used to process the average rate of change on the standardized rate of change S _w,i (t) and S _z,i (t), so as to obtain the standardized average rate of change V _w (t) of the leading index and the consistent index The standardized average rate of change V _z (t); the seventh sub-calculation module is used to perform synthetic calculations based on the standardized average rate of change V _w (t) of the leading index and the standardized average rate of change V _z (t) of the consistent index, to Obtain the leading synthetic index I _w (t) and the consistent synthetic index I _z (t), where, $I_z\left(t\right)=I_z(t-1)\&times;\frac{200+V_z\left(t\right)}{200-V_z\left(t\right)},$ And I _w (1)=100, I _z (1)=100.

Further, the eighth sub-processing module includes: a ninth sub-processing module, which is used to respectively convert the standardized rate of change S _w,i (t) of the leading index and the standardized rate of change S _z,i (t) of the consistent index through the following formula Perform average rate of change processing to obtain the average rate of change R _w (t) of leading indicators and the average rate of change R _z (t) of consistent indicators:

$R_w\left(t\right)=\frac{\underset{i=1}{\overset{k_w}{\mathrm{\&Sigma;}}}{S}_{w,i}\left(t\right){\mathrm{\&lambda;}}_{w,i}}{\underset{i=1}{\overset{k_w}{\mathrm{\&Sigma;}}}{\mathrm{\&lambda;}}_{w,i}},$ $R_z\left(t\right)=\frac{\underset{i=1}{\overset{k_z}{\mathrm{\&Sigma;}}}{S}_{z,i}\left(t\right){\mathrm{\&lambda;}}_{z,i}}{\underset{i=1}{\overset{k_Z}{\mathrm{\&Sigma;}}}{\mathrm{\&lambda;}}_{z,i}},$ Among them, λw _,i and λz _,i are the weights of the leading indicator and the i-th indicator of the consistent indicator respectively; the eighth sub-calculation module is used to obtain the indicator standardization factor F _w through the following formula: $f_w=\left[\underset{t=2}{\overset{\mathrm{no}}{\mathrm{\&Sigma;}}}\right|R_w\left(t\right)|/(\mathrm{no}-1)]/\left[\underset{t=2}{\overset{\mathrm{no}}{\mathrm{\&Sigma;}}}\right|R_z\left(t\right)|/(\mathrm{no}-1)];$ The ninth sub-computing module is used to process the standardized average rate of change according to the index standardization factor _Fw , so as to obtain the standardized average rate of change Vw ( _{t) of the leading index and the standardized average rate of change Vz (} t) of the consistent index, where , V _w (t)=R _w (t)/F _w , V _z (t)=R _z (t).

Further, after executing the acquisition module, the device further includes: a tenth sub-processing module, which is used to preprocess the data in the data sequence, and the preprocessing includes: filling missing data processing, correcting noise data processing, data smoothing processing, and data Normalized processing.

Through the method and device for obtaining early warning parameters of electric power demand of the present application, after obtaining the trend item and cycle item in the original data sequence, the leading index and the consistent index are obtained by screening and calculating the data sequence, and then the above leading index and the consistent The index is synthesized with the index synthesis model to obtain the early warning parameters, and the periodic fluctuation of power demand is analyzed according to the early warning parameters, which solves the problem of using forecasting technology to obtain short-term periodic fluctuation distortion in the field of power demand in the prior art, and the early warning parameters that meet the power demand cannot be obtained. , so that it is impossible to formulate reasonable measures to deal with periodic fluctuations according to the periodic fluctuations of power, and realize the accurate acquisition of early warning parameters of power demand, so as to accurately formulate reasonable and scientific countermeasures according to short-term periodic fluctuations, thereby slowing down the cycle The magnitude of fluctuations can reduce the damage caused by cyclical fluctuations to the power industry and economic development.

Description of drawings

The accompanying drawings described here are used to provide a further understanding of the present invention and constitute a part of the application. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations to the present invention. In the attached picture:

Fig. 1 is a schematic diagram according to the periodic fluctuation of power demand in the prior art;

Fig. 2 is a schematic structural diagram of a device for obtaining early warning parameters of power demand according to the present invention;

3 is a flow chart of a method for obtaining early warning parameters of power demand according to an embodiment of the present invention;

4 is a detailed flow chart of a method for obtaining early warning parameters of power demand according to an embodiment of the present invention;

FIG. 5 is a schematic diagram of a method for obtaining consistent indicators and leading indicators according to the embodiment shown in FIG. 4; and

FIG. 6 is a schematic diagram of trend adjustment according to the embodiment shown in FIG. 4 .

detailed description

It should be noted that, in the case of no conflict, the embodiments in the present application and the features in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and examples.

Fig. 2 is a schematic structural diagram of a device for obtaining early warning parameters of power demand according to the present invention. As shown in Figure 2, the device includes: an acquisition module 10, used to acquire a data sequence for generating early warning indicators; a first processing module 30, used to filter the data sequence according to the adjustment parameters, to obtain the data sequence containing trend items and The data sequence of the periodic item; the first calculation module 50 is used to calculate the trend index of the data sequence containing the trend item and the periodic item, and filter the data sequence containing the trend item and the periodic item according to the trend index to obtain an early warning The index sequence, the early warning index sequence is the data whose trend index is increasing in the data sequence; the first extraction module 70 is used to extract the power generation in the early warning index sequence as the benchmark index, and extract the indexes other than the electricity consumption as the selected index ; The second calculation module 90 is used to calculate the correlation of the early warning indicators according to the time difference analysis model and/or the K-L information model, so as to obtain the correlation coefficient between each selected index and the benchmark index, and according to the correlation coefficient Screen the selected indicators to obtain leading indicators and consistent indicators; the second processing module 110 is used to synthesize the leading indicators and consistent indicators according to the composite index model to obtain the leading composite index and consistent composite index as early warning parameters.

Using the device for obtaining early warning parameters of power demand of the present invention, the data sequence used to generate early warning indicators will be obtained through the acquisition module, and then the first processing module will filter the data sequence and obtain the data sequence containing trend items and period items , then the first calculation module calculates the trend index of the above data sequence, and filters the above data sequence according to the trend index to obtain the early warning index sequence with the trend index in the data sequence growing, and then extracts the early warning index sequence through the first extraction module The power generation of the system is used as the benchmark index, and the indicators other than electricity consumption are extracted as the selected indicators, and then the second calculation module performs correlation calculation on the early warning indicators according to the time difference analysis model and/or the K-L information model, so as to obtain each selected indicator. Select the correlation coefficient between the index and the benchmark index, and screen the selected index according to the correlation coefficient to obtain the leading index and the consistent index, and finally the second processing module synthesizes the leading index and the consistent index according to the synthetic index model, To obtain the leading composite index and consistent composite index as early warning parameters. Through the device for obtaining early warning parameters of electric power demand in the present application, the trend item and period item in the data sequence are obtained first, and then the data sequence is processed to obtain the leading index and the consistent index, and the above leading index and the consistent index are combined into an index synthesis model The early warning parameters are synthesized to solve the problem in the prior art that using forecasting technology to obtain short-term periodic fluctuations in the field of power demand cannot obtain early warning parameters that meet power demand, resulting in the inability to formulate reasonable countermeasures based on periodic power fluctuations , to realize the accurate acquisition of early warning parameters of power demand, so as to accurately formulate reasonable countermeasures according to short-term periodic fluctuations, thereby slowing down the magnitude of periodic fluctuations and reducing the degree of damage caused by periodic fluctuations to the power industry and economic development.

Specifically, in the field of electricity demand, the data sequence used to generate early warning indicators includes four parts: long-term trend items, cycle items caused by periodic laws such as industry expansion-prosperity contraction-recession-re-expansion, spring, summer, autumn and winter or the number of days per month Seasonal items caused by different factors, random items caused by unknown factors. Through the device of the present invention for obtaining the early warning parameters of electric power demand, the seasonal item and the random item in the original data sequence are first eliminated, and the data sequence of the trend item and the cycle item is obtained, and then the quarterly GDP or the monthly industrial added value As a benchmark index, use time difference correlation analysis or K-L information technology to screen several leading indicators and consistent indicators from a large number of other economic indicators, and finally use the index synthesis model to synthesize several leading indicators into leading indexes respectively, and combine several consistent indicators Synthesized into a consensus index, and obtained the early warning parameters, the purpose of judging the future trend of the consensus index by using the advanced nature of the leading index can be realized according to the early warning parameters.

In the above-mentioned embodiments of the present invention, the data sequence is screened by the first processing module 30, and the original data sequence is seasonally adjusted, that is, the influence of seasonal factors and random factors is eliminated, and the long-term trend item (that is, in the above-mentioned embodiment) Based on the trend item) and the short-term cycle item (that is, the cycle item in the above-mentioned embodiment), through index screening and index synthesis, the fluctuation trend of power demand is studied with the boom early warning technology, and the above-mentioned embodiment uses the economic early warning power demand, Select leading indicators and consistent indicators from a large number of economic indicators to judge the periodic fluctuations of power demand, making the judgment results more accurate and making the periodic fluctuations used to formulate reasonable countermeasures more accurate and reasonable.

In the above practical example of the present application, the second calculation module 90 includes: a first sub-calculation module, which is used to obtain the correlation coefficient r _l between each selected index and the benchmark index according to the following formula:

Among them, l=0,±1,±2,…,±12, Y=(y ₁ ,y ₂ ,…,y _n ) is the benchmark index, X=(x ₁ ,x ₂ ,…,x _n ) is Selected indicators, l is the time difference, n _l is the number of all indicators; the first sub-processing module is used to select the selected indicators whose time difference value is within the first value range and the correlation coefficient r _l is greater than the first threshold As the leading index, the selected index whose time difference value is within the second value range and whose correlation coefficient r _l is greater than the second threshold is taken as the consistent index.

Specifically, the benchmark indicators are used as the "benchmark" for screening, and the time difference correlation analysis model is used to initially screen consistent indicators and leading indicators. All the selected indexes except the benchmark indexes lead or lag period l (i.e. the time difference in the above-mentioned embodiment) (l=0, ±1, ±2,..., ±12), the first sub-calculation module is respectively according to the following formula Calculate the correlation coefficient r _l between each selected index and the benchmark index:

$r_l=\frac{\underset{t=1}{\overset{{\mathrm{no}}_l}{\mathrm{\&Sigma;}}}(x_{t+l}-\stackrel{\&OverBar;}{x})({\mathrm{the\; y}}_t-\stackrel{\&OverBar;}{\mathrm{the\; y}})}{\sqrt{\underset{t=1}{\overset{{\mathrm{no}}_l}{\mathrm{\&Sigma;}}}{(x_{t+l}-\stackrel{\&OverBar;}{x})}^2{({\mathrm{the\; y}}_t-\stackrel{\&OverBar;}{\mathrm{the\; y}})}^2}},$ l=0, ±1, ±2, ..., ±12,

In the above formula, Y=(y ₁ ,y ₂ ,…,y _n ) is the benchmark index, X=(x ₁ ,x ₂ ,…,x _n ) is the selected index, r is the correlation coefficient, l means leading or lagging Period (instantaneous difference), when l is negative, it means ahead, when it is positive, it means lag, l is called time difference or delay number, and are the mean values of sequences X and Y, respectively. n _l is the number of data of all indicators. Then the largest time-difference correlation coefficient is considered to reflect the time-difference correlation relationship between the selected index and the benchmark index, and the corresponding delay number l represents the leading or lagging period, that is, the largest delay number l of the time-difference correlation coefficient r _l is the selected index The lead or lag period with respect to the benchmark metric.

According to the above-mentioned embodiment of the present application, the second calculation module 90 may further include: a second sub-calculation module, configured to obtain the correlation coefficient r _l between each selected index and the benchmark index according to the following formula:

Among them, l=0,±1,±2,…,±12, Y=(y ₁ ,y ₂ ,…,y _n ) is the benchmark index, X=(x ₁ ,x ₂ ,…,x _n ) is Selected indicators, l is the time difference, n _l is the number of all indicators; the second sub-processing module is used to select the selected indicators whose time difference value is within the first value range and the correlation coefficient r _l is greater than the first threshold As the initial index of the leading index, the selected index whose time difference value is within the second value range and the correlation coefficient r _l is greater than the second threshold is used as the initial index of the consistent index; the third sub-processing module is used for benchmarking Indicators, the initial indicators of the leading indicators and the initial indicators of the consistent indicators are standardized to obtain the standard benchmark index sequence p _t and the sequence q _t of the standard selected indicators, where the standard selected indicators include the standard leading indicators and the standard consistent indicators; The third sub-calculation module is used to obtain the KL information amount k _l between each standard selected index and the standard benchmark index according to the following formula:

k _l =∑p _t ln(p _t /q _t+1 ), where, l=0,±1,…,±12, t=1, 2,..., n, l is the time difference, n _l is the number of all indicators; the fourth sub-processing module is used to set the time difference value within the third value range and the KL information amount k _l is less than the first The three-threshold standard selects the index as the leading index, and takes the selected index whose time difference value is within the fourth value range and the KL information k _l is less than the fourth threshold as the consistent index. Wherein, the first value range may be less than -3, the second value range may be greater than or equal to -2 and less than or equal to 2, the first threshold may be 0.7, and the second threshold may also be 0.7. Among them, t is the number of months.

Specifically, after the correlation coefficient r _l is obtained through the calculation of the second sub-calculation module, the second sub-processing module uses the selected index whose time difference value is within the first value range and the correlation coefficient r _l is greater than the first threshold as The initial index of the leading index, and the selected index whose time difference value is within the second value range and the correlation coefficient r _l is greater than the second threshold is used as the initial index of the consistent index, and then the third sub-processing module standardizes the benchmark index Processing, the processed standard benchmark index sequence is denoted as p _t :

$p_t={\mathrm{the\; y}}_t/\underset{t=1}{\overset{\mathrm{no}}{\mathrm{\&Sigma;}}}{\mathrm{the\; y}}_t,$ t=1,2,...,n,

The third sub-processing module also standardizes the pre-selected leading indicators and consistent indicators, and the processed standard is recorded as the sequence of selected indicators as q _t :

$q_t=x_t/\underset{t=1}{\overset{\mathrm{no}}{\mathrm{\&Sigma;}}}{x}_t,$ t=1,2,...,n,

Then, the fourth sub-processing module calculates the amount of KL information k _l about the benchmark index after the delay l of each primary index:

k _l =∑p _t ln(p _t /q _t+1 ), l=0,±1,…,±12

Among them, l represents the leading or lagging period. When l is a negative number, it means leading, and when it is positive, it means lagging. _l is called the time difference. In the formula, t represents the month and j represents the year.

More specifically, after calculating 2L+1 KL information amounts, select a minimum value k _l′ from the k _l values as the KL information amount of the selected index x with respect to the benchmark index y, namely The corresponding delay number l ^* is the most appropriate leading or lagging months (quarters) of the selected indicators. The closer the KL information is to 0, the closer the index x is to the benchmark index y. And the screened leading indicators and consistent indicators are recorded as W ⁽³⁾ and Z ⁽³⁾ respectively.

According to the above-mentioned embodiments of the present application, the second processing module 110 may include: a fifth sub-processing module, configured to perform symmetrical change processing on the leading index and the consistent index respectively, so as to obtain the symmetric change rate C _w,i (t) of the leading index And consistent index symmetric rate of change C _{z, i} (t), the fifth sub-calculation module includes: the fourth sub-calculation module, used to carry out symmetric change processing to the leading index through the following formula, to obtain the leading index symmetric rate of change C _{w, i} (t):

in, is the number of the i (i=1, 2, ..., k _w ) leading indicators, t=2, 3, ..., n, k _w leading indicators; and the fifth sub-calculation module, used for the following formula The consistent index is processed symmetrically to obtain the symmetrical change rate C _z,i (t) of the consistent index:

in, is the i-th (i=1, 2, ..., k _z ) consistent index, t=2, 3, ..., n, k _z is the number of consistent indicators; the sixth sub-processing module is used to be symmetrical to the leading index The results obtained after standardization and trend adjustment of the rate of change C _w,i (t) and the symmetrical rate of change of the consistent index C _z,i (t) are combined and calculated to obtain the leading composite index and the consistent composite index.

Specifically, the fifth sub-processing module uses the exponential synthesis model to find the symmetrical rate of change of the index and standardizes it through the sixth sub-processing module, and the fourth sub-calculation module in the fifth sub-processing module according to the following formula Find the symmetrical rate of change C _w,i (t):

t=2,3,...,n, where, is the i-th (i=1, 2, . . . , k _w ) leading indicator, and k _w is the number of leading indicators.

Through the fifth sub-calculation module according to the following formula Find the symmetrical rate of change C _z,i (t):

t=2,3,...,n, where, is the i-th (i=1, 2, ..., k _z ) consistent index, and k _z is the number of consistent indexes.

In the above-mentioned embodiment of the present application, the sixth sub-processing module may include: a sixth sub-calculation module, which is used to obtain the normalization factors A _{w, i} and A _{z, i} through the following formula: t=2,3,...,n; the seventh sub-processing module is used to use normalization factors A _{w, i} and A _{z, i} to respectively convert the leading index symmetric rate of change C _w,i (t) and the consistent index symmetric rate of change C _z,i (t) is standardized to obtain standardized rate of change S _w,i (t) and S _z,i (t), where,

$S_{w,i}\left(t\right)=\frac{C_{w,i}\left(t\right)}{A_{w,i}},$ $S_{z,i}\left(t\right)=\frac{C_{z,i}\left(t\right)}{A_{z,i}},$ t=2,3,...,n;

The eighth sub-processing module is used to process the average rate of change on the standardized rate of change S _w,i (t) and S _z,i (t), so as to obtain the standardized average rate of change V _w (t) of the leading index and the consistent index The standardized average rate of change V _z (t); the seventh sub-calculation module is used to perform synthetic calculations based on the standardized average rate of change V _w (t) of the leading index and the standardized average rate of change V _z (t) of the consistent index, to Obtain the leading synthetic index I _w (t) and the consistent synthetic index I _z (t), where, $I_z\left(t\right)=I_z(t-1)\&times;\frac{200+V_z\left(t\right)}{200-V_z\left(t\right)},$ And I _w (1)=100, I _z (1)=100.

Specifically, according to the following formula, the sixth sub-calculation module performs standardized calculations on the symmetrical rate of change C _w,i (t) of the leading index and the symmetrical rate of change C _z,i (t) of the consistent index, so that the average absolute value is equal to 1, and the obtained Normalization factors A _w,i and A _z,i :

$A_{w,i}=\underset{t=2}{\overset{\mathrm{no}}{\mathrm{\&Sigma;}}}\frac{\left|C_{w,i}\left(t\right)\right|}{\mathrm{no}-1},$ $A_{z,i}=\underset{t=2}{\overset{\mathrm{no}}{\mathrm{\&Sigma;}}}\frac{\left|C_{z,i}\left(t\right)\right|}{\mathrm{no}-1},$ t=2,3,...,n,

Then the seventh sub-processing module standardizes C _{w, i} (t) and C _{z, i} (t) according to A _{w, i} and A _{z, i} respectively, and obtains standardized rate of change S _{w, i} (t) and S _{z, i} (t):

t=2,3,...,n, then the eighth sub-processing module performs average change rate processing according to standardized change rates S _w,i (t) and S _z,i (t) to obtain the standardized average change rate of leading indicators V _w (t) and the standardized average rate of change V _z (t) of the consistent index, and through the seventh sub-calculation module according to the standardized average rate of change V _w (t) of the leading index and the standardized average rate of change V _z ( t) Perform composite calculations to obtain the leading composite index I _w (t) and the consistent composite index I _z (t), specifically: let I _w (1)=100, I _z (1)=100, then

${\mathrm{<span\; class="notranslate">\; I</span>}}_{\mathrm{<span\; class="notranslate">\; w</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; I</span>}}_{\mathrm{<span\; class="notranslate">\; w</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&times;</span>\frac{\mathrm{<span\; class="notranslate">\; 200</span>}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; V</span>}}_{\mathrm{<span\; class="notranslate">\; w</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; 200</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; V</span>}}_{\mathrm{<span\; class="notranslate">\; w</span>}}\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; ,</span>$ ${\mathrm{<span\; class="notranslate">\; I</span>}}_{\mathrm{<span\; class="notranslate">\; z</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; I</span>}}_{\mathrm{<span\; class="notranslate">\; z</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&times;</span>\frac{\mathrm{<span\; class="notranslate">\; 200</span>}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; V</span>}}_{\mathrm{<span\; class="notranslate">\; z</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; 200</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; V</span>}}_{\mathrm{<span\; class="notranslate">\; z</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; .</span>$

More specifically, after obtaining the leading composite index I _w (t) and the consistent composite index I _z (t), the power demand trend can be adjusted in the following way:

According to the following compound interest formula, calculate the respective average growth rate for each sequence of the consistent index group:

$r_i=(\sqrt[m_i]{C_{\mathrm{Li}}/C_{\mathrm{II}}}-1)\&times;100,$ i=1,2,…,k _z ,

Wherein, Fig. 6 is a schematic diagram of trend adjustment according to the embodiment shown in Fig. 4, as shown in Fig. 6, t is the month, and are the average values of the first and last cycles of the i-th index of the consistent index group, m _Ii and m _Li are the months of the first and last cycles of the i-th index of the consistent index group, k ₂ is the number of consistent indicators, m _i is the number of months between the center of the first cycle to the center of the last cycle.

Then calculate the average growth rate G _r of the consistent index group and use it as the target trend: Then use the compound interest formula to calculate their respective average growth rates r′ _w and r′ _z for the initial composite indices I _w (t) and I _z (t) of the leading and consistent indicators:

${\mathrm{<span\; class="notranslate">\; r</span>}}_{\mathrm{<span\; class="notranslate">\; w</span>}}^{<span\; class="notranslate">\; \&prime;</span>}<span\; class="notranslate">\; (</span>\sqrt[{\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; w</span>}}]{{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; Lw</span>}}<span\; class="notranslate">\; /</span>{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; Iw</span>}}}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&times;</span>\mathrm{<span\; class="notranslate">\; 100</span>}<span\; class="notranslate">\; ,</span>$ ${\mathrm{<span\; class="notranslate">\; r</span>}}_{\mathrm{<span\; class="notranslate">\; z</span>}}^{<span\; class="notranslate">\; \&prime;</span>}<span\; class="notranslate">\; (</span>\sqrt[{\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; z</span>}}]{{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; Lz</span>}}<span\; class="notranslate">\; /</span>{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; Iz</span>}}}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&times;</span>\mathrm{<span\; class="notranslate">\; 100</span>}<span\; class="notranslate">\; ,</span>$

in,

Then make trend adjustments for the standardized average rate of change V _w (t) and V _z (t) of the leading index group and the consistent index group:

V′ _w (t)=V _w (t)+(G _r -r′ _w ), V′ _z (t)=V _z (t)+(G _r -r′ _z ), t=2,3, ..., n. Then calculate composite index according to the method in above-mentioned embodiment: make I' _w (1)=100, I' _z (1)=100, then

${{\mathrm{<span\; class="notranslate">\; I</span>}}^{<span\; class="notranslate">\; \&prime;</span>}}_{\mathrm{<span\; class="notranslate">\; w</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>{{\mathrm{<span\; class="notranslate">\; I</span>}}^{<span\; class="notranslate">\; \&prime;</span>}}_{\mathrm{<span\; class="notranslate">\; w</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&times;</span>\frac{\mathrm{<span\; class="notranslate">\; 200</span>}<span\; class="notranslate">\; +</span>{{\mathrm{<span\; class="notranslate">\; V</span>}}^{<span\; class="notranslate">\; \&prime;</span>}}_{\mathrm{<span\; class="notranslate">\; w</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; 200</span>}<span\; class="notranslate">\; -</span>{{\mathrm{<span\; class="notranslate">\; V</span>}}^{<span\; class="notranslate">\; \&prime;</span>}}_{\mathrm{<span\; class="notranslate">\; w</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; ,</span>$ ${{\mathrm{<span\; class="notranslate">\; I</span>}}^{<span\; class="notranslate">\; \&prime;</span>}}_{\mathrm{<span\; class="notranslate">\; z</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>{{\mathrm{<span\; class="notranslate">\; I</span>}}^{<span\; class="notranslate">\; \&prime;</span>}}_{\mathrm{<span\; class="notranslate">\; z</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&times;</span>\frac{\mathrm{<span\; class="notranslate">\; 200</span>}<span\; class="notranslate">\; +</span>{{\mathrm{<span\; class="notranslate">\; V</span>}}^{<span\; class="notranslate">\; \&prime;</span>}}_{\mathrm{<span\; class="notranslate">\; z</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; 200</span>}<span\; class="notranslate">\; -</span>{{\mathrm{<span\; class="notranslate">\; V</span>}}^{<span\; class="notranslate">\; \&prime;</span>}}_{\mathrm{<span\; class="notranslate">\; z</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; ,</span>$

Generate the leading composite index CI _w (t) and consistent composite index CI _z (t) with the base year as 100:

${\mathrm{<span\; class="notranslate">\; CI</span>}}_{\mathrm{<span\; class="notranslate">\; w</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; I</span>}}_{\mathrm{<span\; class="notranslate">\; w</span>}}^{<span\; class="notranslate">\; \&prime;</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; /</span>\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{{\mathrm{<span\; class="notranslate">\; I</span>}}_{\mathrm{<span\; class="notranslate">\; w</span>}}^{<span\; class="notranslate">\; \&prime;</span>}}<span\; class="notranslate">\; \&times;</span>\mathrm{<span\; class="notranslate">\; 100</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ,</span>$ ${\mathrm{<span\; class="notranslate">\; CI</span>}}_{\mathrm{<span\; class="notranslate">\; z</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; I</span>}}_{\mathrm{<span\; class="notranslate">\; z</span>}}^{<span\; class="notranslate">\; \&prime;</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; /</span>\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{{\mathrm{<span\; class="notranslate">\; I</span>}}_{\mathrm{<span\; class="notranslate">\; z</span>}}^{<span\; class="notranslate">\; \&prime;</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&times;</span>\mathrm{<span\; class="notranslate">\; 100</span>}<span\; class="notranslate">\; ,</span>$

in and are the mean values of I′ _w (t) and I′ _z (t) in the base year, respectively.

According to the above-mentioned embodiments of the present application, the eighth sub-processing module may include: a ninth sub-processing module, which is used to respectively convert the normalized rate of change S _w,i (t) of the leading index and the standardized rate of change S of the consistent index through the following formula _z,i (t) performs average rate of change processing to obtain the average rate of change R _w (t) of leading indicators and the average rate of change R _z (t) of consistent indicators:

$R_w\left(t\right)=\frac{\underset{i=1}{\overset{k_w}{\mathrm{\&Sigma;}}}{S}_{w,i}\left(t\right){\mathrm{\&lambda;}}_{w,i}}{\underset{i=1}{\overset{k_w}{\mathrm{\&Sigma;}}}{\mathrm{\&lambda;}}_{w,i}},$ $R_z\left(t\right)=\frac{\underset{i=1}{\overset{k_z}{\mathrm{\&Sigma;}}}{S}_{z,i}\left(t\right){\mathrm{\&lambda;}}_{z,i}}{\underset{i=1}{\overset{k_z}{\mathrm{\&Sigma;}}}{\mathrm{\&lambda;}}_{z,i}},$ Among them, λw _,i and λz _,i are the weights of the leading indicator and the i-th indicator of the consistent indicator respectively; the eighth sub-calculation module is used to obtain the indicator standardization factor F _w through the following formula:

${\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; w</span>}}<span\; class="notranslate">\; =</span><span\; class="notranslate">\; [</span>\underset{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 2</span>}}{\overset{\mathrm{<span\; class="notranslate">\; no</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}<span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; w</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; |</span><span\; class="notranslate">\; /</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ]</span><span\; class="notranslate">\; /</span><span\; class="notranslate">\; [</span>\underset{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 2</span>}}{\overset{\mathrm{<span\; class="notranslate">\; no</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}<span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; z</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; |</span><span\; class="notranslate">\; /</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ]</span><span\; class="notranslate">\; ;</span>$

The ninth sub-calculation module is used to process the standardized average rate of change according to the index normalization factor _Fw , so as to obtain the standardized average rate of change Vw ₍ t) of the leading index and the standardized average rate of change _V2 (t) of the consistent index, where , V _w (t)=R _w (t)/F _w , V _z (t)=R _z (t).

Specifically, the standardized rate of change S _w,i (t) of the leading indicator and the standardized rate of change S _z,i (t) of the consistent indicator are processed by the average rate of change respectively by the following formula to obtain the average rate of change R of the leading indicator _w (t) and the average rate of change R _z (t) of the consensus indicator:

$R_w\left(t\right)=\frac{\underset{i=1}{\overset{k_w}{\mathrm{\&Sigma;}}}{S}_{w,i}\left(t\right){\mathrm{\&lambda;}}_{w,i}}{\underset{i=1}{\overset{k_w}{\mathrm{\&Sigma;}}}{\mathrm{\&lambda;}}_{w,i}},$ $R_z\left(t\right)=\frac{\underset{i=1}{\overset{k_z}{\mathrm{\&Sigma;}}}{S}_{z,i}\left(t\right){\mathrm{\&lambda;}}_{z,i}}{\underset{i=1}{\overset{k_Z}{\mathrm{\&Sigma;}}}{\mathrm{\&lambda;}}_{z,i}},$ λ _w,i and λ _z,i are the weights of the i-th index of the leading and consistent index groups, respectively.

Then calculate the exponential normalization factor F _w according to the following formula:

${\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; w</span>}}<span\; class="notranslate">\; =</span><span\; class="notranslate">\; [</span>\underset{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 2</span>}}{\overset{\mathrm{<span\; class="notranslate">\; no</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}<span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; w</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; |</span><span\; class="notranslate">\; /</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ]</span><span\; class="notranslate">\; /</span><span\; class="notranslate">\; [</span>\underset{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 2</span>}}{\overset{\mathrm{<span\; class="notranslate">\; no</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}<span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; z</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; |</span><span\; class="notranslate">\; /</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ]</span><span\; class="notranslate">\; ;</span>$

Finally, the normalized average rate of change is processed according to the index standardization factor F _w to obtain the standardized average rate of change V _w (t) of the leading index and the standardized average rate of change V _z (t) of the consistent index:

V _w (t) = R _w (t)/F _w , V _z (t) = R _z (t), t = 2, 3,..., n, where the amplitude of the average rate of change of the consistent index sequence is used to The purpose of adjusting the average rate of change of the leading index series and the lagging index series is to apply the two indexes as a coordinated system.

According to the above-mentioned embodiments of the present application, after executing the acquisition module 10, the device may further include: a tenth sub-processing module, for preprocessing the data in the data sequence, the preprocessing includes: filling missing data processing, correcting noise data processing, data smoothing, and data normalization.

Fig. 3 is a flow chart of a method for obtaining early warning parameters of power demand according to an embodiment of the present invention. Fig. 4 is a detailed flow chart of a method for obtaining early warning parameters of power demand according to an embodiment of the present invention.

As shown in Figure 3 and Figure 4, the method includes the following steps:

Step S102, acquiring a data sequence for generating early warning indicators.

Wherein, this step can be realized by step S202 in FIG. 4 : collecting monthly data on macroeconomics and power demand, and performing preprocessing.

In step S104, the data series are screened according to the adjustment parameters to obtain a data series including trend items and period items. Wherein, this method can be implemented through step S204 in FIG. 4: by making seasonal adjustments to the data series, the trend items and period items of all indicators are obtained.

Specifically, in the field of electricity demand, the data sequence for generating early warning indicators includes four parts: long-term trend items (ie trend items), cycle items caused by periodic laws such as industry expansion-prosperity contraction-recession-re-expansion (ie cycle items) Seasonal items caused by different seasons, spring, summer, autumn and winter or the number of days per month, random items caused by unknown factors, seasonal adjustments are made to the indicators of data value and physical quantities, seasonal items and random items in the data sequence are eliminated, and trend items are obtained and a data sequence of periodic terms.

Step S106, calculate the trend index of the data sequence containing trend items and period items, and filter the data series containing trend items and period items according to the trend index to obtain the early warning index sequence, which is the trend in the data sequence Exponential is the growth data. Among them, this step can be realized through steps S206 to S208: step S206 judges whether the index data in the data sequence is growth index index data, and if yes, executes step S208: calculate the growth rate of the index, and in the data sequence In the case that the index data is not growth index index data, step S210 is performed.

Step S108, extracting the power generation amount in the early warning index sequence as the reference index, and extracting the indexes other than the power generation amount as the selected indexes. Wherein, the method is realized through step S210: determining a benchmark index.

Specifically in the field of electricity demand, quarterly GDP or monthly industrial added value is used as the benchmark indicator.

Step S110, perform correlation calculation on the warning indicators according to the time difference analysis model and/or the K-L information model, so as to obtain the correlation coefficient between each selected index and the benchmark index, and filter the selected indicators according to the correlation coefficient , to get the leading and consistent indicators. Among them, step S212 can implement this method: use the time difference analysis model to screen the initial consistent indicators and initial leading indicators; then perform step S214: use the K-L information model to screen the final consistent indicators and leading indicators from the primary consistent indicators and leading indicators .

Specifically, the time-difference analysis model can be used to preliminarily screen consistent indicators and leading indicators from indicators such as macroeconomics and industrial product output.

In step S112, the leading index and the consistent index are synthesized according to the synthetic index model, so as to obtain the leading synthetic index and the consistent synthetic index as early warning parameters. Among them, the above method is realized by executing step S216: using the index synthesis model to synthesize the consistent index and the leading index into the consistent index and the leading index; Demand fluctuates periodically.

Specifically, use the index synthesis model to synthesize several leading indicators into a leading index, and synthesize several consistent indicators into a consistent index. After obtaining the early warning parameters, the future of the consistent indicators can be judged based on the early warning parameters. purpose of the trend.

Using the method for obtaining early warning parameters of electric power demand of the present invention, the data sequence obtained for generating early warning indicators is screened, and the data sequence containing trend items and period items is obtained, and then the trend index of the above data sequence is calculated, and Filter the data sequence according to the trend index to obtain the early warning index sequence in which the trend index is increasing in the data sequence, then extract the power generation in the early warning index sequence as the benchmark index, and extract the indicators other than electricity consumption as the selected indicators , and then calculate the correlation of the early warning indicators according to the time difference analysis model and/or the K-L information model, so as to obtain the correlation coefficient between each selected index and the benchmark index, and filter the selected indicators according to the correlation coefficient, In order to obtain the leading index and the consistent index, finally, according to the composite index model, the leading index and the consistent index are synthesized to obtain the leading composite index and the consistent composite index as early warning parameters. Through the method of obtaining early warning parameters of electric power demand in this application, the trend items and period items in the data sequence are obtained first, and then the leading indicators and consistent indicators are obtained through screening, and the above leading indicators and consistent indicators are synthesized with an index synthesis model to obtain early warning Parameters, which solves the problem in the prior art that using forecasting technology to obtain short-term periodic fluctuation distortion in the field of power demand, and the early warning parameters that meet the power demand cannot be obtained, resulting in the inability to formulate reasonable countermeasures according to the periodic fluctuation of power. Realized Accurately obtain the early warning parameters of power demand, so as to accurately formulate reasonable measures to deal with periodic fluctuations according to the effect of short-term periodic fluctuations, thereby slowing down the magnitude of periodic fluctuations and reducing the damage caused by periodic fluctuations to the power industry and economic development.

In the above-mentioned embodiment of the present invention, the data sequence is firstly screened through step S102, that is, the original data sequence is seasonally adjusted, that is, the influence of seasonal factors and random factors is removed, and the long-term trend item (that is, in the above-mentioned embodiment Based on the trend item) and the short-term cycle item (that is, the cycle item in the above-mentioned embodiment), through index screening and index synthesis, the fluctuation trend of power demand is studied with the boom early warning technology, and the above-mentioned embodiment uses the economic early warning power demand, Select leading indicators and consistent indicators from a large number of economic indicators to judge the periodic fluctuations of power demand, making the judgment results more accurate and making the periodic fluctuations used to formulate reasonable countermeasures more accurate and reasonable.

Wherein, the preprocessing in step S202 may include: filling missing data processing, correcting noise data processing, data smoothing processing and data normalization processing, and the processed data sequence is taken as Y ⁽⁰⁾ .

Specifically, in the above-mentioned embodiments of the present application, before performing step S204, the method further includes the following steps:

(1) Remove the difference in the number of working days in a given month between different years caused by holidays or other reasons. The specific method is as follows:

In this example, suppose the monthly data series to be analyzed (t=1,2,...,n) there are m years, n months, n=m×12, t represents the number of months, i represents the number of weeks, j represents the number of years, then the actual number of working days in each month is D _t (t =1, 2,...,n), the average number of working days in each month in m years is: (L=1,2,…,12), the working days adjustment coefficient sequence p _t can be obtained:

t=1, 2,...,n, and then adjust the coefficient p _t according to the number of working days to get the adjusted monthly data series Y ⁽¹⁾ : $Y_t^{\left(1\right)}=Y_t^{\left(0\right)}/p_t.$

(2) Adjust the working day in the sequence: extract the changes caused by the different working days (week structure) of each month from the original sequence.

Assume that the variable elements of the working day of the week are included in the irregular elements, that is, the form of the irregular elements is ID _r , assuming that ID _r has been decomposed from the original sequence, use regression analysis to find the corresponding weights of Monday, Tuesday, ..., days , so that the ID _r is decomposed into the real irregular element I and the weekly working day variable element D _r .

${\mathrm{<span\; class="notranslate">\; ID</span>}}_{\mathrm{<span\; class="notranslate">\; rt</span>}}\text{<span class="notranslate"> -1.0=</span>}\frac{{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}\mathrm{<span\; class="notranslate">\; t</span>}}{\mathrm{<span\; class="notranslate">\; B</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; t</span>}}{\mathrm{<span\; class="notranslate">\; B</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; +</span><span\; class="notranslate">\; \&CenterDot;</span><span\; class="notranslate">\; \&Center\; Dot;</span><span\; class="notranslate">\; \&Center\; Dot;</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; 7</span>}\mathrm{<span\; class="notranslate">\; t</span>}}{\mathrm{<span\; class="notranslate">\; B</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}}{{\mathrm{<span\; class="notranslate">\; A</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; I</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; ,</span>$

In the above formula: ID _rt is an irregular element including the variable element D _r of weekday in month t; x _it is the number of days in week i in month t (t=1,...,n); B _i is the number of days in week i Weights( ); A _t is the number of days in month t, 28.25 days in February; I _t is the real irregular element.

When the estimated value of _Bi thus obtained is _bi , the variable element D _r of the weekly working day in month t can be calculated according to the following formula:

D _rt ={x _1t (b ₁ +1)+x _2t (b ₂ +1)+...+x _7t (b ₇ +1)}/A _t .

(3) After adjusting the data series according to factors of holidays and working days, correct the peculiar items in the series.

When decomposing various factors in the economic time series, it is necessary to pre-correct items with significant outliers in irregular changes (that is, idiosyncratic items, such as strikes, the impact of bad weather, data errors, etc.). The method is:

a. The setting of the threshold value of the unique item.

Assume that the irregular element I has been decomposed from the original sequence. In order to exclude outliers in the irregular variable element I, it is necessary to calculate the standard deviation of the 5-year moving average of I. First calculate the initial 5-year moving average standard deviation which is:

${\mathrm{\&sigma;}}_j^0=\sqrt{\frac{1}{60}\underset{t=j\&times;12-36+1}{\overset{j\&times;12+\mathrm{twenty\; four}}{\mathrm{\&Sigma;}}}{(I_t-{\stackrel{\&OverBar;}{I}}_j)}^2},$ j=3,4,...,m-2

In the above formula is the 5-year moving average of the I series, m is the number of years in the I series, t=1,2,...,n (t is the number of months in the I series). Let Corresponding to the central year of the 5-year period, one is calculated for each year so is an annual sequence. can be considered satisfied The I _t is specific, remove these I _t , by the following formula:

${\mathrm{\&sigma;}}_j=\sqrt{\frac{1}{60-a}\mathrm{\&Sigma;}{(I_t-{\stackrel{\&OverBar;}{I}}_j)}^2},$ j=3,4,...,m-2,

Calculate the 5-year moving standard deviation {σ _j } again, where t=1,2,...,n (t is the number of months in the I series), and a is the number of outliers. Two items are missing at both ends of the {σ _j } sequence, and the {σ _j } of the 3rd year from the start and end are used to replace the missing two-year σ _j values at both ends.

b. The correction of the peculiar items.

The modified weight w is calculated according to the 5-year moving standard deviation {σ _j },

Among them, t=1,2,…,n (t is the number of months in the I sequence), j=1,2,…,m, using the above inequalities can correct the peculiar term of the I sequence: It corresponds to w _t <1 , with this w _t as the weight, the two items of I _t-2 , I _t-1 , I _t+1 , I _t+2 that are similar to it before and after (note that the w corresponding to the selected item must be equal to 1 , otherwise take the value next to it) as a weighted average of 5 items, and use the value obtained in this way Replace I _t . If I _t corresponding to w _t <1 is located at both ends, take this w _t as the weight, and a total of 4 items of the I value of the three items w _t = 1 close to it are used as weighted average, and the obtained average value is used Replace I _t . The I sequence after correcting the specific items is recorded as I ^w .

(4) According to the monthly data series Make an initial estimate to obtain the initial trend cycle component.

Estimating the Cyclic Component of Trend in a Series Using a Centered 12-Term Moving Average

${\mathrm{<span\; class="notranslate">\; TC</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; (</span>\frac{{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 6</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 5</span>}}<span\; class="notranslate">\; +</span><span\; class="notranslate">\; \&Center\; Dot;</span><span\; class="notranslate">\; \&Center\; Dot;</span><span\; class="notranslate">\; \&Center\; Dot;</span><span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; +</span><span\; class="notranslate">\; \&CenterDot;</span><span\; class="notranslate">\; \&Center\; Dot;</span><span\; class="notranslate">\; \&Center\; Dot;</span><span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 5</span>}}}{\mathrm{<span\; class="notranslate">\; 12</span>}}<span\; class="notranslate">\; +</span>\frac{{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 5</span>}}<span\; class="notranslate">\; +</span><span\; class="notranslate">\; \&CenterDot;</span><span\; class="notranslate">\; \&Center\; Dot;</span><span\; class="notranslate">\; \&Center\; Dot;</span><span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; +</span><span\; class="notranslate">\; \&CenterDot;</span><span\; class="notranslate">\; \&CenterDot;</span><span\; class="notranslate">\; \&CenterDot;</span><span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 5</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 6</span>}}}{\mathrm{<span\; class="notranslate">\; 12</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ,</span>$

$<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; twenty\; four</span>}}{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 6</span>}}<span\; class="notranslate">\; +</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; 12</span>}}{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 5</span>}}<span\; class="notranslate">\; +</span><span\; class="notranslate">\; \&Center\; Dot;</span><span\; class="notranslate">\; \&Center\; Dot;</span><span\; class="notranslate">\; \&Center\; Dot;</span><span\; class="notranslate">\; +</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; 12</span>}}{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; +</span><span\; class="notranslate">\; \&Center\; Dot;</span><span\; class="notranslate">\; \&Center\; Dot;</span><span\; class="notranslate">\; \&CenterDot;</span><span\; class="notranslate">\; +</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; 12</span>}}{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 5</span>}}<span\; class="notranslate">\; +</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; twenty\; four</span>}}{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 6</span>}}$

Among them, y _t-6 , y _t-5 , …, y _t , …, y _t+5 , y _t+6 are monthly data series elements in .

(5) Cycle ingredients according to trend Estimated Seasonal Irregularity Component

(6) Irregular composition according to season Apply a 3×3 moving average to each month to initially estimate the seasonal component:

First, according to the following formula, the seasonal factor is obtained

${\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; S</span>}}}_{\mathrm{<span\; class="notranslate">\; t</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; 3</span>}}<span\; class="notranslate">\; (</span>\frac{{\mathrm{<span\; class="notranslate">\; Si</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; twenty\; four</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; Si</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 12</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; Si</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>}}{\mathrm{<span\; class="notranslate">\; 3</span>}}<span\; class="notranslate">\; +</span>\frac{{\mathrm{<span\; class="notranslate">\; Si</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 12</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; Si</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; Si</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 12</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>}}{\mathrm{<span\; class="notranslate">\; 3</span>}}<span\; class="notranslate">\; +</span>\frac{{\mathrm{<span\; class="notranslate">\; Si</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; Si</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 12</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; Si</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; twenty\; four</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>}}{\mathrm{<span\; class="notranslate">\; 3</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ;</span>$

$\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; 9</span>}}{\mathrm{<span\; class="notranslate">\; Si</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; twenty\; four</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; +</span>\frac{\mathrm{<span\; class="notranslate">\; 2</span>}}{\mathrm{<span\; class="notranslate">\; 9</span>}}{\mathrm{<span\; class="notranslate">\; Si</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 12</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; +</span>\frac{\mathrm{<span\; class="notranslate">\; 3</span>}}{\mathrm{<span\; class="notranslate">\; 9</span>}}{\mathrm{<span\; class="notranslate">\; Si</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; +</span>\frac{\mathrm{<span\; class="notranslate">\; 2</span>}}{\mathrm{<span\; class="notranslate">\; 9</span>}}{\mathrm{<span\; class="notranslate">\; Si</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 12</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; +</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; 9</span>}}{\mathrm{<span\; class="notranslate">\; Si</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; twenty\; four</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>}$

Then the seasonal factor is standardized and calculated to obtain the standard seasonal factor so that the factors sum to approximately zero for each consecutive 12-month period:

${\mathrm{<span\; class="notranslate">\; S</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; =</span>{\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; S</span>}}}_{\mathrm{<span\; class="notranslate">\; t</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; twenty\; four</span>}}{\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; S</span>}}}_{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 6</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; +</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; 12</span>}}{\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; S</span>}}}_{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 5</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; +</span><span\; class="notranslate">\; \&CenterDot;</span><span\; class="notranslate">\; \&CenterDot;</span><span\; class="notranslate">\; \&CenterDot;</span><span\; class="notranslate">\; +</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; 12</span>}}{\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; S</span>}}}_{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 5</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; +</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; twenty\; four</span>}}{\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; S</span>}}}_{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 6</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; .</span>$

(7) According to the standard seasonal factor Initially estimated seasonally adjusted series

(8) Use the 13-item moving average to further estimate the trend cycle component

${\mathrm{<span\; class="notranslate">\; TC</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; 16796</span>}}<span\; class="notranslate">\; (</span><span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; 375</span>}\mathrm{<span\; class="notranslate">\; TCI</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 6</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; 468</span>}\mathrm{<span\; class="notranslate">\; TCI</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 5</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; 1100</span>}\mathrm{<span\; class="notranslate">\; TCI</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 3</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 2475</span>}\mathrm{<span\; class="notranslate">\; TC</span>}{\mathrm{<span\; class="notranslate">\; I</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 2</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; 3600</span>}\mathrm{<span\; class="notranslate">\; TCI</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>}$

$<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; 4032</span>}\mathrm{<span\; class="notranslate">\; TCI</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; 3600</span>}\mathrm{<span\; class="notranslate">\; TCI</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; 2475</span>}\mathrm{<span\; class="notranslate">\; TCI</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 2</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; 1100</span>}\mathrm{<span\; class="notranslate">\; TCI</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 3</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; 468</span>}\mathrm{<span\; class="notranslate">\; TCI</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 5</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; 325</span>}\mathrm{<span\; class="notranslate">\; TCI</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 6</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; .</span>$

(9) Further estimate seasonal irregularities:

(10) Estimate the final seasonal component with a 3×5 moving average:

Obtain the final seasonal factor according to the following formula

${\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; S</span>}}}_{\mathrm{<span\; class="notranslate">\; t</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; 15</span>}}{\mathrm{<span\; class="notranslate">\; Si</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 36</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; +</span>\frac{\mathrm{<span\; class="notranslate">\; 2</span>}}{\mathrm{<span\; class="notranslate">\; 15</span>}}{\mathrm{<span\; class="notranslate">\; Si</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; twenty\; four</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; +</span>\frac{\mathrm{<span\; class="notranslate">\; 3</span>}}{\mathrm{<span\; class="notranslate">\; 15</span>}}{\mathrm{<span\; class="notranslate">\; Si</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 12</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; +</span>\frac{\mathrm{<span\; class="notranslate">\; 3</span>}}{\mathrm{<span\; class="notranslate">\; 15</span>}}{\mathrm{<span\; class="notranslate">\; Si</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; +</span>\frac{\mathrm{<span\; class="notranslate">\; 3</span>}}{\mathrm{<span\; class="notranslate">\; 15</span>}}{\mathrm{<span\; class="notranslate">\; Si</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 12</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; +</span>\frac{\mathrm{<span\; class="notranslate">\; 2</span>}}{\mathrm{<span\; class="notranslate">\; 15</span>}}{\mathrm{<span\; class="notranslate">\; Si</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; twenty\; four</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; +</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; 15</span>}}{\mathrm{<span\; class="notranslate">\; Si</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 36</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>}$

Then for the seasonal factor Normalize to get the final standardized seasonal factor Make the factors sum to approximately zero for each consecutive 12-month period:

${\mathrm{<span\; class="notranslate">\; S</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; =</span>{\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; S</span>}}}_{\mathrm{<span\; class="notranslate">\; t</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; twenty\; four</span>}}{\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; S</span>}}}_{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 6</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; +</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; 12</span>}}{\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; S</span>}}}_{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 5</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; +</span><span\; class="notranslate">\; \&CenterDot;</span><span\; class="notranslate">\; \&CenterDot;</span><span\; class="notranslate">\; \&CenterDot;</span><span\; class="notranslate">\; +</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; 12</span>}}{\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; S</span>}}}_{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 5</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; +</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; twenty\; four</span>}}{\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; S</span>}}}_{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 6</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; .</span>$

(11) According to the standardized final seasonal factor Estimating the Seasonally Adjusted Series

(12) Obtain the final trend cycle component, the method is as follows:

First estimate the irregular component:

Then use the irregular term and trending items The ratio of the sum of the absolute values of the monthly growth rates to measure the significance of the irregular components

$\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{\mathrm{<span\; class="notranslate">\; I</span>}}<span\; class="notranslate">\; /</span>\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{\mathrm{<span\; class="notranslate">\; C</span>}}<span\; class="notranslate">\; =</span>\frac{\underset{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 2</span>}}{\overset{\mathrm{<span\; class="notranslate">\; no</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}<span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; I</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; /</span>{{\mathrm{<span\; class="notranslate">\; I</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>}}^{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; |</span>}{\underset{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 2</span>}}{\overset{\mathrm{<span\; class="notranslate">\; no</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}<span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; TC</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>}{{\mathrm{<span\; class="notranslate">\; TC</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>}}^{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; |</span>}<span\; class="notranslate">\; ,</span>$

if Then use the following 9-term moving average to estimate the final trend cycle component:

${\mathrm{<span\; class="notranslate">\; TC</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 3</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; 2431</span>}}<span\; class="notranslate">\; (</span><span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; 99</span>}\mathrm{<span\; class="notranslate">\; TCI</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 4</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; twenty\; four</span>}\mathrm{<span\; class="notranslate">\; TCI</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 3</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; 288</span>}\mathrm{<span\; class="notranslate">\; TCI</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 2</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; 348</span>}\mathrm{<span\; class="notranslate">\; TCI</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; 805</span>}\mathrm{<span\; class="notranslate">\; TCI</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; ;</span>$

$<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 648</span>}{\mathrm{<span\; class="notranslate">\; TCI</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; 288</span>}\mathrm{<span\; class="notranslate">\; TCI</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 2</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; twenty\; four</span>}\mathrm{<span\; class="notranslate">\; TCI</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 3</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; 99</span>}\mathrm{<span\; class="notranslate">\; TCI</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 4</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; )</span>$

if The final trend cycle component is then estimated using the following 13-term moving average:

${\mathrm{<span\; class="notranslate">\; TC</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 3</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; 16796</span>}}<span\; class="notranslate">\; (</span><span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; 325</span>}\mathrm{<span\; class="notranslate">\; TCI</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 6</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 468</span>}\mathrm{<span\; class="notranslate">\; TC</span>}{\mathrm{<span\; class="notranslate">\; I</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 5</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; 1100</span>}\mathrm{<span\; class="notranslate">\; TCI</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 3</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; 2475</span>}\mathrm{<span\; class="notranslate">\; TCI</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 2</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; 3600</span>}\mathrm{<span\; class="notranslate">\; TCI</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; ;</span>$

$<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; 4032</span>}\mathrm{<span\; class="notranslate">\; TCI</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; 3600</span>}\mathrm{<span\; class="notranslate">\; TCI</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; 2475</span>}\mathrm{<span\; class="notranslate">\; TCI</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 2</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; 1100</span>}\mathrm{<span\; class="notranslate">\; TCI</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 3</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; 468</span>}\mathrm{<span\; class="notranslate">\; TCI</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 5</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; 325</span>}\mathrm{<span\; class="notranslate">\; TCI</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 6</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; )</span>$

if Then use the following 23-term moving average to estimate the final trend cycle component:

${\mathrm{<span\; class="notranslate">\; TC</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 3</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; 4032015</span>}}<span\; class="notranslate">\; (</span><span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; 17250</span>}\mathrm{<span\; class="notranslate">\; TCI</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 11</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; 44022</span>}\mathrm{<span\; class="notranslate">\; TCI</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 10</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; 63250</span>}\mathrm{<span\; class="notranslate">\; TCI</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 9</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; 5757</span>}\mathrm{<span\; class="notranslate">\; TCI</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 8</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; 19950</span>}\mathrm{<span\; class="notranslate">\; TCI</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 7</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; )</span>$

$<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; 54150</span>}\mathrm{<span\; class="notranslate">\; TCI</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 6</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 156978</span>}{\mathrm{<span\; class="notranslate">\; TCI</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 5</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; 275400</span>}\mathrm{<span\; class="notranslate">\; TCI</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 4</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; 392700</span>}\mathrm{<span\; class="notranslate">\; TCI</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 3</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; 491700</span>}\mathrm{<span\; class="notranslate">\; TCI</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 2</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; 557700</span>}\mathrm{<span\; class="notranslate">\; TCI</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; ,</span>$

$<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 580853</span>}{\mathrm{<span\; class="notranslate">\; TCI</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; 557700</span>}\mathrm{<span\; class="notranslate">\; TCI</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; 491700</span>}\mathrm{<span\; class="notranslate">\; TCI</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 2</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; 392700</span>}\mathrm{<span\; class="notranslate">\; TCI</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 3</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; 275400</span>}\mathrm{<span\; class="notranslate">\; TCI</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 4</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; 156978</span>}\mathrm{<span\; class="notranslate">\; TCI</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 5</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>}$

$<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 54150</span>}{\mathrm{<span\; class="notranslate">\; TCI</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 6</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; 19950</span>}\mathrm{<span\; class="notranslate">\; TCI</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 7</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; 58575</span>}\mathrm{<span\; class="notranslate">\; TCI</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 8</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; 63250</span>}\mathrm{<span\; class="notranslate">\; TCI</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 9</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; 44022</span>}\mathrm{<span\; class="notranslate">\; TCI</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 10</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; 17250</span>}\mathrm{<span\; class="notranslate">\; TCI</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 11</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; )</span>$

The final trend cycle component is obtained through the above calculation

(13) Cycle ingredients according to trend and the seasonally adjusted series Estimate the final irregular composition: $I_t^{\left(3\right)}={\mathrm{TCI}}_t^{\left(2\right)}-{\mathrm{TC}}_t^{\left(3\right)}.$

Then the sequence Y ⁽²⁾ =TC ⁽³⁾ containing only trend cycle items after seasonal adjustment is obtained.

In the above-mentioned embodiment of the present application, step S108 in FIG. 4 is specifically realized by the following method: for the physical quantity and the value quantity index, calculate the development index of the sequence after seasonal adjustment: is the indicator of the development index, without transformation, that is, Y ⁽³⁾ =Y ⁽²⁾ .

In the above-mentioned embodiments of the present application, step S110 is performed: performing correlation calculation on the early warning indicators according to the time difference analysis model and/or the K-L information model, so as to obtain the correlation coefficient between each selected indicator and the benchmark indicator, and The process of screening the selected indicators according to the correlation coefficient to obtain leading indicators and consistent indicators includes the following steps:

Obtain the correlation coefficient r _l between each selected index and the benchmark index according to the following formula:

Among them, l=0,±1,±2,…,±12, Y=(y ₁ ,y ₂ ,…,y _n ) is the benchmark index, X=(x ₁ ,x ₂ ,…,x _n ) is Selected indicators, l is the time difference, specifically the leading or lagging period, n _l is the number of all indicators; the selected indicators whose time difference is within the first value range and the correlation coefficient r _l is greater than the first threshold As the leading index, the selected index whose time difference value is within the second value range and whose correlation coefficient r _l is greater than the second threshold is taken as the consistent index. Wherein, the first value range may be less than -3, the first threshold may be 0.7, the second value range may be greater than or equal to -2 and less than or equal to 2, and the second threshold may be 0.7.

Specifically, in this step, benchmark indicators are used as the "benchmark" for screening, and the time difference correlation analysis model is used to initially screen consistent indicators and leading indicators. All the selected indicators except the benchmark indicators are ahead or lagged by l period (i.e. the time difference in the above-mentioned embodiment) (l=0, ±1, ±2,..., ±12), and each selected index is calculated according to the following formula The correlation coefficient r _l between the index and the benchmark index:

$r_l=\frac{\underset{t=1}{\overset{{\mathrm{no}}_l}{\mathrm{\&Sigma;}}}(x_{t+l}-\stackrel{\&OverBar;}{x})({\mathrm{the\; y}}_t-\stackrel{\&OverBar;}{\mathrm{the\; y}})}{\sqrt{\underset{t=1}{\overset{{\mathrm{no}}_l}{\mathrm{\&Sigma;}}}{(x_{t+l}-\stackrel{\&OverBar;}{x})}^2{({\mathrm{the\; y}}_t-\stackrel{\&OverBar;}{\mathrm{the\; y}})}^2}},$ l=0, ±1, ±2, ..., ±12,

In the above formula, Y=(y ₁ ,y ₂ ,…,y _n ) is the benchmark index, X=(x ₁ ,x ₂ ,…,x _n ) is the selected index, r is the correlation coefficient, l means leading or lagging Period (instantaneous difference), when l is negative, it means ahead, when it is positive, it means lag, l is called time difference or delay number, and are the mean values of sequences X and Y, respectively. n _l is the number of data of all indicators. Then the largest time-difference correlation coefficient is considered to reflect the time-difference correlation relationship between the selected index and the benchmark index, and the corresponding delay number l represents the leading or lagging period, that is, the largest delay number l of the time-difference correlation coefficient r _l is the selected index The lead or lag period with respect to the benchmark metric.

Fig. 5 is a schematic diagram of a method for obtaining a consistent index and a leading index according to the embodiment shown in Fig. 4. Specifically, after obtaining the correlation coefficient r _l , the leading index and the consistent index are obtained by the method shown in Fig. 5:

Step S302: Detect whether the index data is time difference<-3 and correlation coefficient>0.7 or whether the index data is -2≤time difference≤2 and correlation coefficient>0.7. Among them, when the time difference of the index data<-3 and the correlation coefficient>0.7 or the index data meets the condition -2≤time difference≤2 and the correlation coefficient>0.7, execute step S304, and if the index data does not meet the time difference<-3 And if the correlation coefficient is >0.7, and if -2≤time difference≤2 and the correlation coefficient>0.7 are not met, execute step S310: discard the index data.

Step S304: Obtain the initial leading index and the initial consistent index, wherein, when the time difference of the index data is <-3 and the correlation coefficient is >0.7, the data is selected as the initial leading index, and when the index data is -2≤time difference≤2 And when the correlation coefficient is >0.7, the index data is selected as the initial consistent index.

In the above-mentioned embodiment of the present application, according to the time difference analysis model and/or the KL information amount model, the correlation calculation is performed on the early warning indicators, and the correlation coefficient between each selected index and the benchmark index is used, and the correlation coefficient is calculated according to the correlation coefficient. The step of screening the selected indicators to obtain the leading indicators and consistent indicators includes: obtaining the correlation coefficient r _l between each selected indicator and the benchmark indicator according to the following formula:

Among them, l=0,±1,±2,…,±12, Y=(y ₁ ,y ₂ ,…,y _n ) is the benchmark index, X=(x ₁ ,x ₂ ,…,x _n ) is Selected indicators, l is the time difference, n _l is the number of all indicators; the selected index whose time difference value is within the first value range and the correlation coefficient r _l is greater than the first threshold is used as the initial indicator of the leading indicator, and The selected index whose time difference value is within the second value range and the correlation coefficient r _l is greater than the second threshold is used as the initial index of the consistent index; standardize the initial index of the benchmark index, the leading index, and the initial index of the consistent index , to obtain the standard benchmark index sequence p _t and the standard selected index sequence q _t , where the standard selected index includes the standard leading index and the standard consistent index; according to the following formula to obtain the relationship between each standard selected index and the standard benchmark index The amount of KL information k _l :

k _l =∑p _t ln(p _t /q _t+1 ), where l=0，±1，…,±12， t=1, 2,..., n, l is the time difference, n _l is the number of all indicators; the standard selected index that the time difference value is within the third value range and the KL information volume k _l is less than the third threshold is taken as The leading index, and the selected index whose time difference value is within the fourth value range and whose KL information volume k _l is less than the fourth threshold is taken as a consistent index. Wherein, the third value range may be less than -3, the third threshold may be 0.3, the fourth value range may be greater than or equal to -2 and less than or equal to 2, and the fourth threshold may be 0.3.

The above method can be realized through the following steps: standardize the benchmark index, and record the processed standard benchmark index sequence as p _t :

$p_t={\mathrm{the\; y}}_t/\underset{t=1}{\overset{\mathrm{no}}{\mathrm{\&Sigma;}}}{\mathrm{the\; y}}_t,$ t=1,2,...,n,

The pre-selected leading indicators and consistent indicators are also standardized, and the sequence of the selected indicators after processing is denoted as q _t :

$q_t=x_t/\underset{t=1}{\overset{\mathrm{no}}{\mathrm{\&Sigma;}}}{x}_t,$ t=1,2,...,n,

Then, the amount of KL information k _l about the benchmark index after the delay l of each primary index is calculated according to the following formula:

k _l =∑p _t ln(p _t /q _t+l )＝0，±1，…,±12

Among them, l represents the leading or lagging period. When l is a negative number, it means leading, and when it is positive, it means lagging. _l is called the time difference. The t in the formula represents the month, and the i represents the week number.

After calculating 2L+1 KL information volume, select a minimum value k _l′ from the k _l value as the KL information volume of the selected index x with respect to the benchmark index y, that is The corresponding delay number l ^* is the most appropriate leading or lagging months (quarters) of the selected index, among which, the closer the KL information is to 0, the closer the index x is to the benchmark index y.

Specifically, the leading indicators and consistent indicators can be screened through the following steps, which are recorded as W ⁽³⁾ and Z ⁽³⁾ respectively:

Step S306: Detect whether the initial leading index is time difference<-3 and K-L information content<0.3 or whether the initial consistent data is -2≤time difference≤2 and K-L information content<0.3. Among them, when the initial leading index meets the time difference<-3 and the K-L information content<0.3 or the initial consistent data meets the condition that -2≤time difference≤2 and the K-L information content<0.3, step S308 is executed. -3 and K-L information volume<0.3, perform step S310: discard index data, and perform step S310: discard index data if the initial consistent data does not meet -2≤time difference≤2 and K-L information volume<0.3.

Step S308: Select a leading indicator and a consistent indicator. Among them, in the case of the time difference of the initial leading index <-3 and the K-L information volume <0.3, the index is selected as the current index. In the case of the initial consistent index data, -2≤time difference≤2 and the K-L information volume<0.3 , select this indicator as the consistent indicator. For example: select the index with delay number l=-2, -3, -4 as the leading index for primary selection, and the index with delay number l=-1, 0, 1 as the consistent index for primary selection.

According to the above-mentioned embodiment of the present application, the step of synthesizing the leading index and the consistent index according to the composite index model to obtain the leading composite index and the consistent composite index as early warning parameters includes: performing symmetrical change processing on the leading index and the consistent index respectively, In order to obtain the symmetrical rate of change C _{w, i} (t) of the leading index and the symmetrical rate of change C _{z, i} (t) of the consistent index, where the symmetrical change of the leading index is processed by the following formula to obtain the symmetrical rate of change C _w of the leading index _{, i} (t):

in, is the number of the i-th (i=1, 2, ..., k _w ) leading indicators, t = 2, 3, ..., n, k _w leading indicators; through the following formula, carry out symmetrical change processing on the consistent indicators to obtain Symmetric rate of change of consistency index C _z,i (t):

in, is the i-th (i=1, 2,...,k _z ) consistent index, t=2,3,...,n, k _z is the number of consistent indexes; the symmetric change rate C _w,i (t ) and the consistent index symmetric rate of change C _z,i (t) are standardized and trend-adjusted, and the results obtained after the synthetic calculation are performed to obtain the leading synthetic index and the consistent synthetic index. Wherein, t in the above formula represents a month.

Specifically, use the exponential synthesis model to find the symmetrical rate of change of the index and standardize it, and use the following formulas to calculate and Find the symmetrical rates of change C _w,i (t) and C _z,i (t):

$C_{w,i}\left(t\right)=200\&times;\frac{W_i^{\left(3\right)}\left(t\right)-W_i^{\left(3\right)}(t-1)}{W_i^{\left(3\right)}\left(t\right)+W_i^{\left(3\right)}(t-1)},$ t=2,3,...,n,

t=2,3,…,n, where, is the i (i=1, 2, ..., k _w ) leading index, is the i-th (i=1, 2, ..., k _z ) consistent index, and k _w and k _z are the numbers of the leading index and the consistent index respectively.

In the above-mentioned embodiments of the present application, the results obtained after standardization and trend adjustment of the leading index symmetric rate of change C _w,i (t) and the consistent index symmetric rate of change C _z,i (t) are synthetically calculated to The steps of obtaining the preceding composite index and the consistent composite index include: obtaining the normalization factors A _w,i and A _z,i by the following formula: t=2,3,...,n; use standardized factors A _w,i and A _z,i to respectively carry out the symmetrical change rate C _w,i (t) of the leading index and the symmetrical change rate C _z,i (t) of the consistent index Standardized processing to obtain the standardized rate of change S _w,i (t) and S _z,i (t), where,

$S_{w,i}\left(t\right)=\frac{C_{w,i}\left(t\right)}{A_{w,i}},$ $S_{z,i}\left(t\right)=\frac{C_{z,i}\left(t\right)}{A_{z,i}},$ t=2,3,...,n;

Perform average rate-of-change processing on the standardized rate of change S _w,i (t) and S _z,i (t) to obtain the standardized average rate of change V _w (t) of the leading index and the standardized average rate of change V _z ( t); according to the normalized average rate of change V _w (t) of the leading index and the normalized average rate of change V _z (t) of the consistent index, the composite calculation is performed to obtain the leading composite index I _w (t) and the consistent composite index I _z ( t), where, $I_w\left(t\right)=I_w(t-1)\&times;\frac{200+V_w\left(t\right)}{200-V_w\left(t\right)},$ $I_z\left(t\right)=I_z(t-1)\&times;\frac{200+V_z\left(t\right)}{200-V_z\left(t\right)},$ And I _w (1)=100, I _z (1)=100.

Specifically, according to the following formula, standardize the leading index symmetric rate of change C _w,i (t) and the consistent index symmetric rate of change C _z,i (t), so that the average absolute value is equal to 1, and the standardized factor A _{w, i} and _Az,i :

I _t , $A_{z,i}=\underset{t=2}{\overset{\mathrm{no}}{\mathrm{\&Sigma;}}}\frac{\left|C_{z,i}\left(t\right)\right|}{\mathrm{no}-1},$ t=2,3,...,n,

Then standardize C _w,i (t) and C _z,i (t) according to A _w,i and A _z,i respectively, and obtain the standardized rate of change S _w,i (t) and S _z,i (t):

t=2,3,...,n, and then carry out the average change rate processing according to the standardized change rate S _w,i (t) and S _z,i (t) to obtain the standardized average change rate V _w (t) of the leading indicator and the standardized average rate of change V _z (t) of the consistent index, and perform synthetic calculations based on the standardized average rate of change V _w (t) of the leading index and the standardized average rate of change V _z (t) of the consistent index to obtain the leading composite index I _w (t) and consistent synthetic index I _z (t), specifically: I _w (1)=100, I _z (1)=100, then

${\mathrm{<span\; class="notranslate">\; I</span>}}_{\mathrm{<span\; class="notranslate">\; w</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; I</span>}}_{\mathrm{<span\; class="notranslate">\; w</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&times;</span>\frac{\mathrm{<span\; class="notranslate">\; 200</span>}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; V</span>}}_{\mathrm{<span\; class="notranslate">\; w</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; 200</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; V</span>}}_{\mathrm{<span\; class="notranslate">\; w</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; ,</span>$ ${\mathrm{<span\; class="notranslate">\; I</span>}}_{\mathrm{<span\; class="notranslate">\; z</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; I</span>}}_{\mathrm{<span\; class="notranslate">\; z</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&times;</span>\frac{\mathrm{<span\; class="notranslate">\; 200</span>}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; V</span>}}_{\mathrm{<span\; class="notranslate">\; z</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; 200</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; V</span>}}_{\mathrm{<span\; class="notranslate">\; z</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; .</span>$

More specifically, after obtaining the leading composite index I _w (t) and the consistent composite index I _z (t), the power demand trend can be adjusted in the following way:

According to the following compound interest formula, calculate the respective average growth rate for each sequence of the consistent index group:

$r_i=(\sqrt[m_i]{C_{\mathrm{Li}}/C_{\mathrm{II}}}-1)\&times;100,$ i=1,2,...,k _z ,

Among them, as shown in Figure 6, t is the month, and are the average values of the first and last cycles of the i-th index of the consistent index group, m _Ii and m _Li are the months of the first and last cycles of the i-th index of the consistent index group, k ₂ is the number of consistent indicators, m _i is the number of months between the center of the first cycle to the center of the last cycle.

Then calculate the average growth rate G _r of the consistent index group and use it as the target trend: Then use the compound interest formula to calculate their respective average growth rates r′ _w and r′ _z for the initial composite indices I _w (t) and I _z (t) of the leading and consistent indicators:

${\mathrm{<span\; class="notranslate">\; r</span>}}_{\mathrm{<span\; class="notranslate">\; w</span>}}^{<span\; class="notranslate">\; \&prime;</span>}<span\; class="notranslate">\; (</span>\sqrt[{\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; w</span>}}]{{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; Lw</span>}}<span\; class="notranslate">\; /</span>{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; Iw</span>}}}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&times;</span>\mathrm{<span\; class="notranslate">\; 100</span>}<span\; class="notranslate">\; ,</span>$ ${\mathrm{<span\; class="notranslate">\; r</span>}}_{\mathrm{<span\; class="notranslate">\; z</span>}}^{<span\; class="notranslate">\; \&prime;</span>}<span\; class="notranslate">\; (</span>\sqrt[{\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; z</span>}}]{{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; Lz</span>}}<span\; class="notranslate">\; /</span>{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; Iz</span>}}}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&times;</span>\mathrm{<span\; class="notranslate">\; 100</span>}<span\; class="notranslate">\; ,</span>$

in,

Then make trend adjustments for the standardized average rate of change V _w (t) and V _z (t) of the leading index group and the consistent index group:

V′ _w (t)=V _w (t)+(Gr-r′ _w ), V′ _z (t)=V _z (t)+(Gr-r′ _z ), t=2,3,…, n. Then calculate composite index according to the method in above-mentioned embodiment: make I' _w (1)=100, I' _z (1)=100, then

${{\mathrm{<span\; class="notranslate">\; I</span>}}^{<span\; class="notranslate">\; \&prime;</span>}}_{\mathrm{<span\; class="notranslate">\; w</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>{{\mathrm{<span\; class="notranslate">\; I</span>}}^{<span\; class="notranslate">\; \&prime;</span>}}_{\mathrm{<span\; class="notranslate">\; w</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&times;</span>\frac{\mathrm{<span\; class="notranslate">\; 200</span>}<span\; class="notranslate">\; +</span>{{\mathrm{<span\; class="notranslate">\; V</span>}}^{<span\; class="notranslate">\; \&prime;</span>}}_{\mathrm{<span\; class="notranslate">\; w</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; 200</span>}<span\; class="notranslate">\; -</span>{{\mathrm{<span\; class="notranslate">\; V</span>}}^{<span\; class="notranslate">\; \&prime;</span>}}_{\mathrm{<span\; class="notranslate">\; w</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; ,</span>$ ${{\mathrm{<span\; class="notranslate">\; I</span>}}^{<span\; class="notranslate">\; \&prime;</span>}}_{\mathrm{<span\; class="notranslate">\; z</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>{{\mathrm{<span\; class="notranslate">\; I</span>}}^{<span\; class="notranslate">\; \&prime;</span>}}_{\mathrm{<span\; class="notranslate">\; z</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&times;</span>\frac{\mathrm{<span\; class="notranslate">\; 200</span>}<span\; class="notranslate">\; +</span>{{\mathrm{<span\; class="notranslate">\; V</span>}}^{<span\; class="notranslate">\; \&prime;</span>}}_{\mathrm{<span\; class="notranslate">\; z</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; 200</span>}<span\; class="notranslate">\; -</span>{{\mathrm{<span\; class="notranslate">\; V</span>}}^{<span\; class="notranslate">\; \&prime;</span>}}_{\mathrm{<span\; class="notranslate">\; z</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; ,</span>$

Generate the leading composite index CI _w (t) and consistent composite index CI _z (t) with the base year as 100:

${\mathrm{<span\; class="notranslate">\; CI</span>}}_{\mathrm{<span\; class="notranslate">\; w</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; I</span>}}_{\mathrm{<span\; class="notranslate">\; w</span>}}^{<span\; class="notranslate">\; \&prime;</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; /</span>\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{{\mathrm{<span\; class="notranslate">\; I</span>}}_{\mathrm{<span\; class="notranslate">\; w</span>}}^{<span\; class="notranslate">\; \&prime;</span>}}<span\; class="notranslate">\; \&times;</span>\mathrm{<span\; class="notranslate">\; 100</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ,</span>$ ${\mathrm{<span\; class="notranslate">\; CI</span>}}_{\mathrm{<span\; class="notranslate">\; z</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; I</span>}}_{\mathrm{<span\; class="notranslate">\; z</span>}}^{<span\; class="notranslate">\; \&prime;</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; /</span>\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{{\mathrm{<span\; class="notranslate">\; I</span>}}_{\mathrm{<span\; class="notranslate">\; z</span>}}^{<span\; class="notranslate">\; \&prime;</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&times;</span>\mathrm{<span\; class="notranslate">\; 100</span>}<span\; class="notranslate">\; ,</span>$

in and are the mean values of I′ _w (t) and I′ _z (t) in the base year, respectively.

According to the above-mentioned embodiment of the present application, the average rate of change processing is performed on the standardized rate of change S _w,i (t) and S _z,i (t), so as to obtain the normalized average rate of change V _w (t) of the leading index and the consistent index The steps of the normalized average rate of change V _z (t) include:

The standardized rate of change S _w,i (t) of the leading index and the standardized rate of change S _z,i (t) of the consistent index are processed by the average rate of change respectively by the following formula to obtain the average rate of change R _w (t ) and the average rate of change R _z (t) of the consensus indicator:

$R_w\left(t\right)=\frac{\underset{i=1}{\overset{k_w}{\mathrm{\&Sigma;}}}{S}_{w,i}\left(t\right){\mathrm{\&lambda;}}_{w,i}}{\underset{i=1}{\overset{k_w}{\mathrm{\&Sigma;}}}{\mathrm{\&lambda;}}_{w,i}},$ $R_z\left(t\right)=\frac{\underset{i=1}{\overset{k_z}{\mathrm{\&Sigma;}}}{S}_{z,i}\left(t\right){\mathrm{\&lambda;}}_{z,i}}{\underset{i=1}{\overset{k_z}{\mathrm{\&Sigma;}}}{\mathrm{\&lambda;}}_{z,i}},$ Among them, λ _w,i and λ _z,i are the weights of the i-th index of the leading index and the consistent index respectively; the index standardization factor F _w is obtained by the following formula:

$f_w=\left[\underset{t=2}{\overset{\mathrm{no}}{\mathrm{\&Sigma;}}}\right|R_w\left(t\right)|/(\mathrm{no}-1)]/\left[\underset{t=2}{\overset{\mathrm{no}}{\mathrm{\&Sigma;}}}\right|R_z\left(t\right)|/(\mathrm{no}-1)];$ According to the standardization factor F _w of the index, the normalized average rate of change is processed to obtain the normalized average rate of change V _w (t) of the leading index and the normalized average rate of change V _z (t) of the consistent index, where V _w (t) = R _w (t)/F _w , V _z (t) = R _z (t).

Specifically, before obtaining the initial synthetic indices I _w (t) and I _z (t), the standardized rate of change S _w,i (t) of the leading index and the standardized rate of change S _z,i of the consistent index are respectively calculated by the following formula (t) Perform average rate of change processing to obtain the average rate of change R _w (t) of leading indicators and the average rate of change R _z (t) of consistent indicators:

$R_w\left(t\right)=\frac{\underset{i=1}{\overset{k_w}{\mathrm{\&Sigma;}}}{S}_{w,i}\left(t\right){\mathrm{\&lambda;}}_{w,i}}{\underset{i=1}{\overset{k_w}{\mathrm{\&Sigma;}}}{\mathrm{\&lambda;}}_{w,i}},$ $R_z\left(t\right)=\frac{\underset{i=1}{\overset{k_z}{\mathrm{\&Sigma;}}}{S}_{z,i}\left(t\right){\mathrm{\&lambda;}}_{z,i}}{\underset{i=1}{\overset{k_Z}{\mathrm{\&Sigma;}}}{\mathrm{\&lambda;}}_{z,i}},$ λ _w,i and λ _z,i are the weights of the i-th indicator of the leading and consistent indicator groups, respectively.

Then calculate the exponential normalization factor F _w according to the following formula:

${\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; w</span>}}<span\; class="notranslate">\; =</span><span\; class="notranslate">\; [</span>\underset{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 2</span>}}{\overset{\mathrm{<span\; class="notranslate">\; no</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}<span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; w</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; |</span><span\; class="notranslate">\; /</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ]</span><span\; class="notranslate">\; /</span><span\; class="notranslate">\; [</span>\underset{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 2</span>}}{\overset{\mathrm{<span\; class="notranslate">\; no</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}<span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; z</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; |</span><span\; class="notranslate">\; /</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ]</span><span\; class="notranslate">\; ;</span>$

Finally, the normalized average rate of change is processed according to the index standardization factor F _w to obtain the standardized average rate of change V _w (t) of the leading index and the standardized average rate of change V _z (t) of the consistent index:

V _w (t)=R _w (t)/F _w , V _z (t)=R _z (t), t=2,3,…,n, where the amplitude of the average rate of change of the consistent index sequence is used to The purpose of adjusting the average rate of change of the leading index series and the lagging index series is to apply the two indexes as a coordinated system.

It should be noted that the steps shown in the flowcharts of the accompanying drawings may be performed in a computer system, such as a set of computer-executable instructions, and that although a logical order is shown in the flowcharts, in some cases, The steps shown or described may be performed in an order different than here.

From the above description, it can be seen that the present invention achieves the following technical effects: through the method and device for obtaining early warning parameters of electric power demand of the present application, after obtaining the trend item and period item in the original data sequence, by analyzing the data Sequence screening and calculation to obtain leading indicators and consistent indicators, and then use the index synthesis model to synthesize the above leading indicators and consistent indicators to obtain early warning parameters, and analyze the periodic fluctuations of power demand according to the early warning parameters, which solves the problem of power demand in the prior art. Forecasting technology obtains short-term periodic fluctuation distortion, and cannot obtain early warning parameters that meet power demand, which leads to the inability to formulate reasonable measures to deal with periodic fluctuations based on periodic power fluctuations. According to the effect of formulating reasonable and scientific countermeasures based on short-term cyclical fluctuations, the magnitude of cyclical fluctuations is slowed down, and the degree of damage caused by cyclical fluctuations to the power industry and economic development is reduced.

Obviously, those skilled in the art should understand that each module or each step of the above-mentioned present invention can be realized by a general-purpose computing device, and they can be concentrated on a single computing device, or distributed in a network formed by multiple computing devices Optionally, they can be implemented with program codes executable by computing devices, thus, they can be stored in storage devices and executed by computing devices, or they can be made into individual integrated circuit modules, or they can be integrated into Multiple modules or steps are fabricated into a single integrated circuit module to realize. As such, the present invention is not limited to any specific combination of hardware and software.

The above descriptions are only preferred embodiments of the present invention, and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and changes. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims (12)

1. A method for obtaining an early warning parameter of a power demand is characterized by comprising the following steps:

acquiring a data sequence for generating an early warning index;

screening the data sequence according to the adjustment parameters to obtain a data sequence containing a trend item and a period item;

calculating the trend index of the data sequence containing the trend item and the periodic item, and filtering the data sequence containing the trend item and the periodic item according to the trend index to obtain an early warning index sequence, wherein the early warning index sequence is data with the trend index increasing in the data sequence;

extracting the generated energy in the early warning index sequence as a reference index, and extracting indexes except the generated energy as selected indexes;

performing correlation calculation on the early warning indexes according to a time difference analysis model and/or a K-L information quantity model to obtain a correlation coefficient between each selected index and the reference index, and screening the selected indexes according to the correlation coefficient to obtain a leading index and a consistent index;

synthesizing the leading index and the consistent index according to a synthetic index model to obtain a leading synthetic index and a consistent synthetic index which are used as early warning parameters;

after obtaining the data sequence for generating the early warning indicator, the method further comprises: preprocessing data in the data sequence, the preprocessing comprising: filling missing data processing, noise data correction processing, data smoothing processing and data normalization processing.

2. The method of claim 1, wherein the step of performing correlation calculation on the early warning indicators by using a time difference analysis model and/or a K-L information content model, performing a correlation coefficient between each selected indicator and the reference indicator, and screening the selected indicators according to the correlation coefficients to obtain leading indicators and consistent indicators comprises:

obtaining a correlation coefficient r between each of the selected indices and the reference index according to the following formula_l：

$r_l=\frac{\underset{t=1}{\overset{n_l}{\&Sigma;}}(x_{t+l}-\stackrel{\&OverBar;}{x})(y_t-\stackrel{\&OverBar;}{y})}{\sqrt{\underset{t=1}{\overset{n_l}{\&Sigma;}}{(x_{t+l}-\stackrel{\&OverBar;}{x})}^2{(y_t-\stackrel{\&OverBar;}{y})}^2}},$

Wherein, l ═ 0, ± 1, ± 2, …, ± 12, Y ═ Y₁,y₂,…,y_n) As a reference index, X ═ X (X)₁,x₂,…,x_n) In order to be the selected index,andrespectively, the average values of the sequences X and Y, l is the time difference, n_lThe number of all indexes is t is 1,2, …, n is month number, x_t+1Selected index for month t +1, y_tIs a reference index of t months;

the time difference value is in a first value range and the correlation coefficient r_lThe selected index larger than the first threshold value is used as the leading index, the time difference value is in a second value range, and the correlation coefficient r_lThe selected index larger than the second threshold value is taken as the coincidence index.

3. The method according to claim 2, wherein the step of performing correlation calculation on the early warning indicators according to a time difference analysis model and/or a K-L information content model, performing correlation coefficient between each selected indicator and the reference indicator, and screening the selected indicators according to the correlation coefficients to obtain leading indicators and consistent indicators comprises:

obtaining a correlation coefficient r between each of the selected indices and the reference index according to the following formula_l：

$r_l=\frac{\underset{t=1}{\overset{n_l}{\&Sigma;}}(x_{t+l}-\stackrel{\&OverBar;}{x})(y_t-\stackrel{\&OverBar;}{y})}{\sqrt{\underset{t=1}{\overset{n_l}{\&Sigma;}}{(x_{t+l}-\stackrel{\&OverBar;}{x})}^2{(y_t-\stackrel{\&OverBar;}{y})}^2}},$

Wherein, l ═ 0, ± 1, ± 2, …, ± 12, Y ═ Y₁,y₂,…,y_n) As a reference index, X ═ X (X)₁,x₂,…,x_n) Is selected index, when l isDifference, n_lThe number of all indexes is t1, 2, …, and n is the number of months;

the time difference value is in a first value range and the correlation coefficient r_lThe selected index larger than the first threshold value is used as the initial index of the leading index, the time difference value is in a second value range, and the correlation coefficient r_lThe selected index larger than a second threshold value is used as an initial index of the consistent index;

standardizing the reference index, the initial index of the prior index and the initial index of the consistent index to obtain a standard reference index sequence p_tAnd a sequence q of criteria selected indices_tWherein the standard selected index comprises a standard leading index and a standard consistent index;

acquiring K-L information quantity K between each standard selected index and the standard reference index according to the following formula_l：

k_l＝∑p_tln(p_t/q_t+1) Wherein l is 0, ± 1, …, ± 12,t is 1,2, …, n is the number of months, l is the time difference, n_lThe number of all indexes is shown;

setting the time difference value in a third value range and the K-L information content K_lSelecting a standard selected index smaller than a third threshold value as the leading index, and setting the time difference value in a fourth value range and the K-L information content K_lThe selected index smaller than the fourth threshold value is taken as the coincidence index.

4. The method according to claim 2 or 3, wherein the step of synthesizing the leading indicators and the consistent indicators according to a composite index model to obtain the leading composite index and the consistent composite index as the early warning parameters comprises:

respectively carrying out symmetrical change processing on the leading index and the consistent index to obtain a leading indexSymmetrical rate of change C_w,i(t) symmetrical rate of change C with respect to the coincidence indicator_z,i(t)，

Wherein, the symmetrical change processing is carried out on the prior index through the following formula to obtain the symmetrical change rate C of the prior index_w,i(t)：

Wherein,is the ith (i ═ 1,2, …, k_w) Leading indicator, t 2,3, …, n, k_wThe number of the leading indexes;

carrying out symmetrical change processing on the consistent index through the following formula to obtain the symmetrical change rate C of the consistent index_z,i(t)：

Wherein,is the ith (i ═ 1,2, …, k_z) A coincidence index, t is 2,3, …, n is the number of months, k_zThe number of the consistent indexes;

symmetrical change rate C of the leading indicator_w,i(t) and the rate of symmetrical change of the coincidence indicator C_z,i(t) performing a synthesis calculation on the result obtained after the normalization processing and the trend adjustment to obtain the leading synthesis index and the consistent synthesis index.

5. The method of claim 4, wherein the leading indicator is symmetrically varied by a rate C_w,i(t) and the rate of symmetrical change of the coincidence indicator C_z,i(t) the step of performing a synthesis calculation of the result obtained after the normalization process and the trend adjustment to obtain the leading synthesis index and the consistent synthesis index includes:

the normalization factor A is obtained by the following formula_w,iAnd A_z,i：t＝2,3,…,n；

Using the normalization factor A_w,iAnd A_z,iRespectively symmetrically changing the leading indicators by a certain ratio C_w,i(t) and the rate of symmetrical change of the coincidence indicator C_z,i(t) normalizing to obtain a normalized rate of change S_w,i(t) and S_z,i(t) wherein,

$S_{w,i}\left(t\right)=\frac{C_{w,i}\left(t\right)}{A_{w,i}},S_{z,i}\left(t\right)=\frac{C_{z,i}\left(t\right)}{A_{z,i}},t=2,3,...,n;$

for the normalized rate of change S_w,i(t) and S_z,i(t) performing an average change rate process to obtain a normalized average change rate V of the leading indicator_w(t) and the normalized mean rate of change of the conformity indicator V_z(t)；

A normalized average change rate V according to the leading indicator_w(t) and the normalized mean rate of change of the conformity indicator V_z(t) performing a synthesis calculation to obtain the advanced synthesis index I_w(t) and consensus synthesis index I_z(t) wherein,and I_w(1)＝100，I_z(1)＝100。

6. The method of claim 5, wherein the normalized rate of change S_w,i(t) and S_z,i(t) performing an average change rate process to obtain a normalized average change rate V of the leading indicator_w(t) and the normalized mean rate of change of the conformity indicator V_zThe step of (t) comprises:

respectively converting the normalized change rate S of the precedent index by the following formula_w,i(t) and the normalized rate of change of the coincidence indicator S_z,i(t) performing an average change rate process to obtain an average change rate R of the leading indicator_w(t) and the average rate of change R of the coincidence indicator_z(t)：

Wherein λ is_w,iAnd λ_z,iWeights of the i-th index, which are the leading index and the consistent index, respectively;

obtaining an index normalization factor F by the following formula_w：

$F_w=\&lsqb;\underset{t=2}{\overset{n}{\&Sigma;}}\left|R_w\left(t\right)\right|/(n-1)\&rsqb;/\&lsqb;\underset{t=2}{\overset{n}{\&Sigma;}}\left|R_z\left(t\right)\right|/(n-1)\&rsqb;;$

Normalizing factor F according to the index_wPerforming normalized average change rate processing to obtain normalized average change rate V of the leading indicator_w(t) and the normalized mean rate of change of the conformity indicator V_z(t) wherein V_w(t)＝R_w(t)/F_w，V_z(t)＝R_z(t)。

7. An apparatus for obtaining an early warning parameter of a power demand, comprising:

the first acquisition module is used for acquiring a data sequence used for generating an early warning index;

the first processing module is used for screening the data sequence according to the adjustment parameters to obtain the data sequence containing the trend item and the period item;

the first calculation module is used for calculating a trend index of the data sequence containing the trend item and the period item, and filtering the data sequence containing the trend item and the period item according to the trend index to obtain an early warning index sequence, wherein the early warning index sequence is data with the trend index increasing in the data sequence;

the first extraction module is used for extracting the generated energy in the early warning index sequence as a reference index and extracting indexes except the generated energy as selected indexes;

the second calculation module is used for performing correlation calculation on the early warning indexes according to a time difference analysis model and/or a K-L information quantity model to obtain a correlation coefficient between each selected index and the reference index, and screening the selected indexes according to the correlation coefficient to obtain a leading index and a consistent index;

the second processing module is used for synthesizing the leading index and the consistent index according to the synthetic index model so as to obtain a leading synthetic index and a consistent synthetic index which are used as early warning parameters;

the device further comprises: a tenth sub-processing module, configured to perform preprocessing on the data in the data sequence, where the preprocessing includes: filling missing data processing, noise data correction processing, data smoothing processing and data normalization processing.

8. The apparatus of claim 7, wherein the second computing module comprises:

a first sub-calculation module for obtaining a correlation coefficient r between each of the selected index and the reference index according to the following formula_l：

Wherein, l ═ 0, ± 1, ± 2, …, ± 12, Y ═ Y₁,y₂,…,y_n) As a reference index, X ═ X (X)₁,x₂,…,x_n) In order to be the selected index,andrespectively, the average values of the sequences X and Y, l is the time difference, n_lThe number of all indexes is t is 1,2, …, n is month number, x_t+1Selected index for month t +1, y_tIs a reference index of t months;

a first sub-processing module, configured to take the time difference value in a first value range and obtain the correlation coefficient r_lThe selected index larger than the first threshold value is used as the leading index, the time difference value is in a second value range, and the correlation coefficient r_lThe selected index larger than the second threshold value is taken as the coincidence index.

9. The apparatus of claim 8, wherein the second computing module comprises:

a second sub-calculation module for obtaining a correlation coefficient r between each of the selected index and the reference index according to the following formula_l：

Wherein, l ═ 0, ± 1, ± 2, …, ± 12, Y ═ Y₁,y₂,…,y_n) As a reference index, X ═ X (X)₁,x₂,…,x_n) Is selected as an index, l is the time difference, n_lThe number of all indexes is t1, 2, …, and n is the number of months;

a second sub-processing module, configured to take the time difference value in a first value range and obtain the correlation coefficient r_lThe selected index larger than the first threshold value is used as the initial index of the leading index, the time difference value is in a second value range, and the correlation coefficient r_lThe selected index larger than a second threshold value is used as an initial index of the consistent index;

a third sub-processing module for processing the reference index and the leading indexThe initial index of the target and the initial index of the consistent index are standardized to obtain a standard reference index sequence p_tAnd a sequence q of criteria selected indices_tWherein the standard selected index comprises a standard leading index and a standard consistent index;

a third sub-calculation module for obtaining K-L information amount K between each standard selected index and the standard reference index according to the following formula_l：

k_l＝∑p_tln(p_t/q_t+1) Wherein l is 0, ± 1, …, ± 12, t is 1,2, …, n is the number of months, l is the time difference, n_lThe number of all indexes is shown;

a fourth sub-processing module, configured to take the time difference value in a third value range and obtain the K-L information amount K_lSelecting a standard selected index smaller than a third threshold value as the leading index, and setting the time difference value in a fourth value range and the K-L information content K_lThe selected index smaller than the fourth threshold value is taken as the coincidence index.

10. The apparatus of claim 8 or 9, wherein the second processing module comprises:

a fifth sub-processing module, configured to perform symmetric change processing on the leading indicator and the consistent indicator respectively to obtain a symmetric change rate C of the leading indicator_w,i(t) symmetrical rate of change C with respect to the coincidence indicator_z,i(t), the fifth sub-processing module comprising:

a fourth sub-calculation module, configured to perform symmetric change processing on the leading indicator through the following formula to obtain a symmetric change rate C of the leading indicator_w,i(t)：

Wherein,is the ith (i ═ 1,2, …, k_w) A leading indicator, t is 2,3, …, n is the number of months, k_wThe number of the leading indexes;

a fifth sub-calculation module, configured to perform symmetric change processing on the consistent index through the following formula to obtain a symmetric change rate C of the consistent index_z,i(t)：

Wherein,is the ith (i ═ 1,2, …, k_z) One index of coincidence, t is 2,3, …, n, k_zThe number of the consistent indexes;

a sixth sub-processing module for symmetrically changing the leading indicator by a rate C_w,i(t) and the rate of symmetrical change of the coincidence indicator C_z,i(t) performing a synthesis calculation on the result obtained after the normalization processing and the trend adjustment to obtain the leading synthesis index and the consistent synthesis index.

11. The apparatus of claim 10, wherein the sixth sub-processing module comprises:

a sixth sub-calculation module for obtaining the normalization factor A by the following formula_w,iAnd A_z,i： t＝2,3,…,n；

A seventh sub-processing module for employing the normalization factorA_w,iAnd A_z,iRespectively symmetrically changing the leading indicators by a certain ratio C_w,i(t) and the rate of symmetrical change of the coincidence indicator C_z,i(t) normalizing to obtain a normalized rate of change S_w,i(t) and S_z,i(t) wherein,

$S_{w,i}\left(t\right)=\frac{C_{w,i}\left(t\right)}{A_{w,i}},S_{z,i}\left(t\right)=\frac{C_{z,i}\left(t\right)}{A_{z,i}},t=2,3,...,n;$

an eighth sub-processing module for processing the normalized rate of change S_w,i(t) and S_z,i(t) processing the average change rate to obtain the standard of the leading indicatorChange average rate of change V_w(t) and the normalized mean rate of change of the conformity indicator V_z(t)；

A seventh sub-calculation module for calculating a normalized average change rate V according to the leading indicator_w(t) and the normalized mean rate of change of the conformity indicator V_z(t) performing a synthesis calculation to obtain the advanced synthesis index I_w(t) and consensus synthesis index I_z(t) wherein,and I_w(1)＝100，I_z(1)＝100。

12. The apparatus of claim 11, wherein the eighth sub-processing module comprises:

a ninth sub-processing module for respectively calculating the normalized change rate S of the precedent index by the following formula_w,i(t) and the normalized rate of change of the coincidence indicator S_z,i(t) performing an average change rate process to obtain an average change rate R of the leading indicator_w(t) and the average rate of change R of the coincidence indicator_z(t)：

Wherein λ is_w,iAnd λ_z,iWeights of the i-th index, which are the leading index and the consistent index, respectively;

an eighth sub-calculation module for obtaining an index normalization factor F by the following formula_w：

$F_w=\&lsqb;\underset{t=2}{\overset{n}{\&Sigma;}}\left|R_w\left(t\right)\right|/(n-1)\&rsqb;/\&lsqb;\underset{t=2}{\overset{n}{\&Sigma;}}\left|R_z\left(t\right)\right|/(n-1)\&rsqb;;$

A ninth sub-calculation module for normalizing factor F according to the index_wPerforming normalized average change rate processing to obtain normalized average change rate V of the leading indicator_w(t) and the normalized mean rate of change of the conformity indicator V_z(t) wherein V_w(t)＝R_w(t)/F_w，V_z(t)＝R_z(t)。