CN117557071B - Sparse time sequence prediction method, device, storage medium and application - Google Patents
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Abstract
The invention relates to the technical field of computer application, and discloses a method, equipment, a storage medium and application for predicting sparse time sequences based on service characteristics, wherein the method comprises the following steps: collecting data and processing data; constructing a two-stage prediction model; the first stage is an integrated classification model, and whether consumption occurs in the future is predicted according to the dimensions of a warehouse and a SKU, and classification results are two types, wherein one type is consumption occurring in the future, and the other type is consumption not occurring; the second stage is a time sequence prediction model, and an asymmetric loss function training TimesNet model is adopted to predict future consumption. And classifying the predicted result into data with consumption in the first stage, and entering a second stage to predict the consumption, wherein the first stage predicts the part with no consumption, and the predicted value is directly assigned to zero. The method not only can save calculation time, but also considers business characteristics, and improves the accuracy of predicting sparse demands.
Description
Technical Field
The invention relates to the technical field of computer application, in particular to a business feature-based sparse time sequence prediction method, equipment, a storage medium and application.
Background
Currently, there are conventional prediction methods and machine learning-based prediction methods for demand prediction commonly used in supply chains. Traditional prediction methods include weighted average, exponential smoothing, holt-windows prediction, autoregressive moving average (ARIMA) prediction, etc., and common machine learning methods include long and short term memory neural network (LSTM) method, recurrent neural network (CNN), decision tree, random forest, etc.
In the field of consumer electronics after-market services, spare part requirements tend to be intermittent and sparse. The following schemes are generally employed to predict sparse consumer electronics after-market component demand. Firstly, a statistical method based on historical data uses past spare part demand data for analysis, and technologies such as time sequence analysis, seasonal decomposition, trend analysis and the like are applied to predict future spare part demands. And secondly, a machine learning prediction model is constructed by using a machine learning algorithm such as a decision tree, a random forest, a neural network and the like, so that the spare part demand is predicted more accurately.
However, these methods have some problems and pain points. The statistical method based on the historical data is suitable for the requirement conditions of relatively stability and strong regularity, but the market change of the electronic products is rapid, and the traditional statistical prediction method is difficult to cope with the conditions of emergencies or new product release and the like. The machine learning prediction model requires adjustment of multiple parameters and super parameters, and takes a lot of time and resources to perform model selection and tuning. This feature of the spare parts and the diversity of supply chain warehouse and spare part combinations, given the constraints of the funding flow, makes existing model predictive effects often less than ideal. How to build a model becomes a challenging task.
Disclosure of Invention
Aiming at the problems of low prediction accuracy or excessive prediction redundancy of the demand of the after-market accessories of the consumer electronic products in the prior art, the invention provides a business feature-based sparse time sequence prediction method, which not only can effectively solve the problem of predicting the sparse demand, but also can improve the prediction accuracy.
The invention relates to a method for predicting a sparse time sequence based on service characteristics, which comprises the following steps:
(1) Collecting data, wherein the data comprises a plurality of groups of product data, and each group of data comprises a data ID, a fitting model SKU, a warehouse, the time to market of the SKU and historical consumption data of each ID;
(2) Processing data, comprising:
intercepting data, namely intercepting historical consumption data with a certain time length according to a to-be-predicted demand time point;
Data aggregation, namely, on the basis of data interception, aggregating consumed data according to time granularity;
construction features the following features are constructed on the basis of data aggregation: the time to market, the number of times of consumption, the maximum consumption, the average demand interval, the square of the demand variation coefficient, the last time interval and the minimum interval; the time length of marketing is the time length from the time of marketing to the time point of interception; the consumption times are the times of non-zero consumption occurrence; the average demand interval is the ratio of the time to market and the number of times of consumption; the calculation formula of the square of the demand variation coefficient is as follows:
Wherein CV 2 represents the square of the coefficient of variation of the demand, sigma represents the standard deviation of consumption, and mu is the average value of consumption;
The last time interval is the time length from the last non-zero consumption to the interception time point; the minimum interval is the minimum value of the non-zero consumption time interval of the front and back two times in each ID history consumption data;
(3) Constructing a two-stage prediction model;
The first stage is an integrated classification model, and whether consumption occurs in the future is predicted according to the dimensions of a warehouse and a SKU, and classification results are two types, wherein one type is consumption occurring in the future, and the other type is consumption not occurring; the integrated classification model comprises a LightGBM classifier, a Boostrap & Bayesian classifier, a SKU & warehouse classifier and a weighted average classifier which are constructed to respectively predict whether consumption occurs; synthesizing classifier prediction results, wherein when the number of classifiers predicted to be consumed exceeds a set threshold, the integrated classification model prediction results are consumed, otherwise, the prediction results are not consumed;
the second stage is a time sequence prediction model, an asymmetric loss function is adopted to train TimesNet models, and future consumption is predicted;
and classifying the predicted result into data with consumption in the first stage, and entering a second stage to predict the consumption, wherein the first stage predicts the part with no consumption, and the predicted value is directly assigned to zero.
Preferably, the processing data further includes: data cleansing, wherein the history is removed from the collected data before data interception, and the ID which is never consumed is removed.
Preferably, the LightGBM classifier includes:
selecting a time point to be predicted, and creating a test set according to the data interception, the data aggregation and the construction characteristics;
selecting several groups of time points, intercepting data, aggregating data and constructing characteristics, combining data, and creating a training set;
Selecting a model entering feature comprising SKU, time to market, warehouse, time to market, consumption times, maximum consumption, average demand interval, square of demand variable coefficient, last time interval, minimum interval and consumption data of last time points;
Defining an optimization target, wherein the optimization target adopts a self-defined asymmetric loss function, and the form is as follows:
wherein loss1 is the self-defined asymmetric loss function, a and b are super parameters, y is a true value, Is a predicted value;
Training LightGBM a model and selecting characteristics, and predicting a testing set after model tuning;
Setting a quantile as a threshold, and when the predicted value is larger than the quantile, determining that the classification result of the LightGBM classifier is consumed, otherwise, determining that no consumption occurs.
Preferably, the Boostrap & bayesian classifier includes:
Calculating statistics P (X|I) and P (I) of each ID through Boostrap sampling, wherein X represents event occurrence consumption, I represents the last time interval, P (X|I) represents the probability of occurrence consumption under the condition that the time interval is I, and P (I) represents the probability of occurrence of the time interval I;
Calculating a probability P (X) of consumption of each ID, wherein the probability is approximately equal to the ratio of the consumption times to the time to market;
According to the Bayesian theorem, calculating the probability of each ID that the time interval is I under the condition that the event X occurs:
Setting a quantile as a threshold, and when P (I|X) is larger than the quantile, the classification result of the Boostrap & Bayesian classifier is that consumption occurs, otherwise, no consumption occurs.
Preferably, the SKU & warehouse classifier includes:
counting the consumption times of each SKU and the number of distributed warehouses;
setting a threshold value, and screening a SKU list with the consumption times and the warehouse being larger than the threshold value;
when the SKU corresponding to the ID is included in the SKU list, the SKU & warehouse classifier classifies the result as consumption, otherwise, no consumption occurs.
Preferably, the weighted average classifier includes:
based on the historical consumption data, a weighted average method is used for predicting future consumption, when the prediction result is greater than 0, the classification result of the weighted average classifier is that consumption occurs, and otherwise, no consumption occurs.
Preferably, in the second stage of step (3), the time series model is TimesNet time series models, which can be referred to as Wu,Haixu,et al."Timesnet:Temporal 2d-variation modeling for general time series analysis."arXivpreprint arXiv:2210.02186(2022).
The asymmetric loss function is in the form of:
And loss is the asymmetric loss function, a and b are super parameters, and N is the number of IDs.
The invention also aims to provide application of the method, which can be applied to predicting the after-market accessory demand of the consumer electronic product and carrying out spare parts according to the prediction result. The future consumption can be predicted more accurately, the accuracy of accessory supply can be effectively improved, and resources are saved.
It is also an object of the present invention to provide an electronic device comprising:
a processor and a memory;
the processor is configured to execute the steps of any of the methods described above by calling a program or instructions stored in the memory.
It is also an object of the present invention to provide a computer readable storage medium having stored thereon a computer program, characterized in that the program when executed by a processor realizes the steps of any of the methods described above.
The invention has the positive progress effects that:
Firstly, a two-stage prediction method is adopted, wherein the first stage is an integrated classification model for predicting whether consumption occurs in the future; the second stage is a time sequence prediction model, and predicts the future consumption. According to the two-stage sparse time sequence prediction method based on the business characteristics, calculation power is saved, only the data which are predicted to be consumed by the first-stage integrated classification model enter the second stage to predict the time sequence, and the data which are predicted to be not consumed in the future by the first stage are not processed, so that calculation time is saved.
Secondly, in the integrated classification model, the classifier does not directly adopt the predicted value as a classification result, but sets a quantile as a threshold value, when the predicted value is greater than the threshold value, the classification result is consumed, and otherwise, no consumption occurs. The method reduces the complexity of adjusting the model parameters to a great extent and enhances the generalization performance of the model.
Moreover, business characteristics and data characteristics are comprehensively considered, and accuracy of predicting sparse requirements is improved. According to the prediction method, the first stage model considers the characteristics of time to market, SKU, warehouse and the like, the second stage model considers the characteristics of time sequence data, and the combination of the service characteristics and the data characteristics improves the prediction accuracy.
Finally, the asymmetric loss function is adopted by part of the classifier and the time sequence prediction model in the integrated classification model, so that the model has flexibility, and the model can be adjusted according to the requirements of a decision maker.
Drawings
FIG. 1 is a flow chart of a method for predicting a sparse time sequence based on business features according to the present invention;
FIG. 2 is a diagram illustrating an exemplary data interception of a training set and a testing set for a LightGBM classifier construction of the present invention.
Detailed Description
The invention is further described below with reference to specific examples and figures. It should be understood that the following examples are illustrative of the present invention and are not intended to limit the scope of the present invention.
Example 1
The method for predicting the sparse time sequence based on the service features, referring to fig. 1, comprises the following steps:
(1) The data is collected and includes a plurality of sets of product data, each set of data including a data ID, a fitting model SKU, a warehouse, a time-to-market for the SKU, and historical usage data for each ID. The data format is not limited and table 1 shows a preferred data format.
Table 1 data example
(2) Processing data, comprising:
Data cleaning, namely eliminating the ID which is never consumed in the history from the collected data before data interception;
intercepting data, namely intercepting historical consumption data with a certain time length according to a to-be-predicted demand time point;
data aggregation, namely, on the basis of data interception, aggregating consumed data according to time granularity; for example, taking 7 days as time granularity, summing the consumption data every 7 days as aggregated consumption data;
construction features the following features are constructed on the basis of data aggregation: the time to market, the number of times of consumption, the maximum consumption, the average demand interval, the square of the demand variation coefficient, the last time interval and the minimum interval; the time length of marketing is the time length from the time of marketing to the time point of interception; the consumption times are the times of non-zero consumption occurrence; the average demand interval is the ratio of the time to market and the number of times of consumption; the calculation formula of the square of the demand variation coefficient is as follows:
Wherein CV 2 represents the square of the coefficient of variation of the demand, sigma represents the standard deviation of consumption, and mu is the average value of consumption;
The last time interval is the time length from the last non-zero consumption to the interception time point; the minimum interval is the minimum value of the non-zero consumption time interval of the front and back two times in each ID history consumption data;
(3) Constructing a two-stage prediction model;
The first stage is an integrated classification model, and whether consumption occurs in the future is predicted according to the dimensions of a warehouse and a SKU, and classification results are two types, wherein one type is consumption occurring in the future, and the other type is consumption not occurring; the integrated classification model comprises a LightGBM classifier, a Boostrap & Bayesian classifier, a SKU & warehouse classifier and a weighted average classifier which are constructed to respectively predict whether consumption occurs; synthesizing classifier prediction results, wherein when the number of classifiers predicted to be consumed exceeds a set threshold, the integrated classification model prediction results are consumed, otherwise, the prediction results are not consumed;
the second stage is a time sequence prediction model, an asymmetric loss function is adopted to train TimesNet models, and future consumption is predicted;
and classifying the predicted result into data with consumption in the first stage, and entering a second stage to predict the consumption, wherein the first stage predicts the part with no consumption, and the predicted value is directly assigned to zero.
Preferably, the LightGBM classifier includes:
selecting a time point to be predicted, and creating a test set according to the data interception, the data aggregation and the construction characteristics; the data interception process can be seen in fig. 2.
Selecting several groups of time points, intercepting data, aggregating data and constructing characteristics, combining data, and creating a training set;
Selecting a model entering feature comprising SKU, time to market, warehouse, time to market, consumption times, maximum consumption, average demand interval, square of demand variable coefficient, last time interval, minimum interval and consumption data of last time points; for example, the consumption data for the last 10 time points is selected.
Defining an optimization target, wherein the optimization target adopts a self-defined asymmetric loss function loss1 in the following form:
Wherein a and b are super parameters, y is a true value, Is a predicted value;
Training LightGBM a model and selecting characteristics, and predicting a testing set after model tuning;
Setting a quantile as a threshold, and when the predicted value is larger than the quantile, determining that the classification result of the LightGBM classifier is consumed, otherwise, determining that no consumption occurs.
For example, lightGBM model learning rate is set to 0.0001, the number of weak classifiers is set to 300, the maximum leaf node number of the tree is set to 32, and the loss function is set to:
taking 60% quantiles as a threshold, and when LightGBM predicted values are larger than the threshold, the predicted result of the LightGBM classifier is that consumption occurs.
Preferably, the Boostrap & bayesian classifier includes:
Calculating statistics P (X|I) and P (I) of each ID through Boostrap sampling, wherein X represents event occurrence consumption, I represents the last time interval, P (X|I) represents the probability of occurrence consumption under the condition that the time interval is I, and P (I) represents the probability of occurrence of the time interval I;
Calculating a probability P (X) of consumption of each ID, wherein the probability is approximately equal to the ratio of the consumption times to the time to market;
According to the Bayesian theorem, calculating the probability of each ID that the time interval is I under the condition that the event X occurs:
Setting a quantile as a threshold, and when P (I|X) is larger than the quantile, the classification result of the Boostrap & Bayesian classifier is that consumption occurs, otherwise, no consumption occurs.
Preferably, the SKU & warehouse classifier includes:
counting the consumption times of each SKU and the number of distributed warehouses;
setting a threshold value, and screening a SKU list with the consumption times and the warehouse being larger than the threshold value;
when the SKU corresponding to the ID is included in the SKU list, the SKU & warehouse classifier classifies the result as consumption, otherwise, no consumption occurs.
Preferably, the weighted average classifier includes:
based on the historical consumption data, a weighted average method is used for predicting future consumption, when the prediction result is greater than 0, the classification result of the weighted average classifier is that consumption occurs, and otherwise, no consumption occurs.
Preferably, in the second stage of step (3), the time series model adopts TimesNet time series models;
The asymmetric loss function is in the form of:
wherein a and b are super parameters, and N is the number of IDs.
The above method may be applied to predicting after-market accessory requirements including, but not limited to, consumer electronic products, and making spare parts based on the prediction. The future consumption can be predicted more accurately, the accuracy of accessory supply can be effectively improved, and resources are saved.
The embodiment of the invention also provides electronic equipment, which comprises one or more processors and a memory.
The processor may be a Central Processing Unit (CPU) or other form of processing unit having data processing and/or instruction execution capabilities, and may control other components in the electronic device to perform the desired functions.
The memory may include one or more computer program products that may include various forms of computer-readable storage media, such as volatile memory and/or non-volatile memory. The volatile memory may include, for example, random Access Memory (RAM) and/or cache memory (cache), and the like. The non-volatile memory may include, for example, read Only Memory (ROM), hard disk, flash memory, and the like. One or more computer program instructions may be stored on the computer readable storage medium that can be executed by a processor to perform the inventory forecasting method and/or other desired functions of any of the embodiments of the present invention described above. Various content such as initial arguments, thresholds, etc. may also be stored in the computer readable storage medium.
In some examples, the electronic device may further include: input devices and output devices, which are interconnected by a bus system and/or other forms of connection mechanisms. The input device may include, for example, a keyboard, a mouse, and the like. The output device can output various information to the outside, including early warning prompt information, braking force and the like. The output means may include, for example, a display, speakers, a printer, and a communication network and remote output devices connected thereto, etc.
In addition, the electronic device may include any other suitable components depending on the particular application.
In addition to the methods and apparatus described above, embodiments of the invention may also be a computer program product comprising computer program instructions which, when executed by a processor, cause the processor to perform the steps of the prediction method as set forth in any of the embodiments of the invention.
The computer program product may write program code for performing operations of embodiments of the present invention in any combination of one or more programming languages, including an object oriented programming language such as Java, C++ or the like and conventional procedural programming languages, such as the C programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device, partly on a remote computing device, or entirely on the remote computing device or server.
Furthermore, embodiments of the present invention may also be a computer-readable storage medium having stored thereon computer program instructions which, when executed by a processor, cause the processor to perform the steps of the inventory forecasting method for after-market vehicle parts provided by any of the embodiments of the present invention.
The computer readable storage medium may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may include, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium would include the following: an electrical connection having one or more wires, a portable disk, a hard disk, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
While the preferred embodiments of the present application have been illustrated and described, the present application is not limited to the embodiments, and various equivalent modifications and substitutions can be made by one skilled in the art without departing from the spirit of the present application, and these equivalent modifications and substitutions are intended to be included in the scope of the present application as defined in the appended claims.
Claims (10)
1. A business feature-based sparse time sequence prediction method is characterized by comprising the following steps:
(1) Collecting data, wherein the data comprises a plurality of groups of product data, and each group of data comprises a data ID, a fitting model SKU, a warehouse, the time to market of the SKU and historical consumption data of each ID;
(2) Processing data, comprising:
intercepting data, namely intercepting historical consumption data with a certain time length according to a to-be-predicted demand time point;
Data aggregation, namely, on the basis of data interception, aggregating consumed data according to time granularity;
construction features the following features are constructed on the basis of data aggregation: the time to market, the number of times of consumption, the maximum consumption, the average demand interval, the square of the demand variation coefficient, the last time interval and the minimum interval; the time length of marketing is the time length from the time of marketing to the time point of interception; the consumption times are the times of non-zero consumption occurrence; the average demand interval is the ratio of the time to market and the number of times of consumption; the calculation formula of the square of the demand variation coefficient is as follows:
Wherein CV 2 represents the square of the coefficient of variation of the demand, sigma represents the standard deviation of consumption, and mu is the average value of consumption;
The last time interval is the time length from the last non-zero consumption to the interception time point; the minimum interval is the minimum value of the non-zero consumption time interval of the front and back two times in each ID history consumption data;
(3) Constructing a two-stage prediction model;
The first stage is an integrated classification model, and whether consumption occurs in the future is predicted according to the dimensions of a warehouse and a SKU, and classification results are two types, wherein one type is consumption occurring in the future, and the other type is consumption not occurring; the integrated classification model comprises a LightGBM classifier, a Boostrap & Bayesian classifier, a SKU & warehouse classifier and a weighted average classifier which are constructed to respectively predict whether consumption occurs; synthesizing classifier prediction results, wherein when the number of classifiers predicted to be consumed exceeds a set threshold, the integrated classification model prediction results are consumed, otherwise, the prediction results are not consumed;
the second stage is a time sequence prediction model, an asymmetric loss function is adopted to train TimesNet models, and future consumption is predicted;
and classifying the predicted result into data with consumption in the first stage, and entering a second stage to predict the consumption, wherein the first stage predicts the part with no consumption, and the predicted value is directly assigned to zero.
2. The method of claim 1, wherein processing the data further comprises: data cleansing, wherein the history is removed from the collected data before data interception, and the ID which is never consumed is removed.
3. The method of claim 1 or 2, wherein the LightGBM classifier comprises:
selecting a time point to be predicted, and creating a test set according to the data interception, the data aggregation and the construction characteristics;
selecting several groups of time points, intercepting data, aggregating data and constructing characteristics, combining data, and creating a training set;
Selecting a model entering feature comprising SKU, time to market, warehouse, time to market, consumption times, maximum consumption, average demand interval, square of demand variable coefficient, last time interval, minimum interval and consumption data of last time points;
Defining an optimization target, wherein the optimization target adopts a self-defined asymmetric loss function, and the form is as follows:
wherein loss1 is the self-defined asymmetric loss function, a and b are super parameters, y is a true value, Is a predicted value;
Training LightGBM a model and selecting characteristics, and predicting a testing set after model tuning;
Setting a quantile as a threshold, and when the predicted value is larger than the quantile, determining that the classification result of the LightGBM classifier is consumed, otherwise, determining that no consumption occurs.
4. The method of claim 1 or 2, wherein the Boostrap & bayesian classifier comprises:
Calculating statistics P (X|I) and P (I) of each ID through Boostrap sampling, wherein X represents event occurrence consumption, I represents the last time interval, P (X|I) represents the probability of occurrence consumption under the condition that the time interval is I, and P (I) represents the probability of occurrence of the time interval I;
Calculating a probability P (X) of consumption of each ID, wherein the probability is approximately equal to the ratio of the consumption times to the time to market;
According to the Bayesian theorem, calculating the probability of each ID that the time interval is I under the condition that the event X occurs:
Setting a quantile as a threshold, and when P (I|X) is larger than the quantile, the classification result of the Boostrap & Bayesian classifier is that consumption occurs, otherwise, no consumption occurs.
5. The method of claim 1 or 2, wherein the SKU & warehouse classifier comprises:
counting the consumption times of each SKU and the number of distributed warehouses;
setting a threshold value, and screening a SKU list with the consumption times and the warehouse being larger than the threshold value;
when the SKU corresponding to the ID is included in the SKU list, the SKU & warehouse classifier classifies the result as consumption, otherwise, no consumption occurs.
6. The method of claim 1 or 2, wherein the weighted average classifier comprises:
based on the historical consumption data, a weighted average method is used for predicting future consumption, when the prediction result is greater than 0, the classification result of the weighted average classifier is that consumption occurs, and otherwise, no consumption occurs.
7. The method of claim 1 or 2, wherein in the second stage of step (3), a time series model is employed as TimesNet time series model;
The asymmetric loss function is in the form of:
And loss is the asymmetric loss function, a and b are super parameters, and N is the number of IDs.
8. Use of the method according to claim 1 or 2 for predicting after-market accessory requirements of consumer electronic products and for making spare parts based on the prediction.
9. An electronic device, the electronic device comprising:
a processor and a memory;
the processor is adapted to perform the steps of the method according to any of claims 1-7 by invoking a program or instruction stored in the memory.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the steps of the method according to any one of claims 1-7.
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