Discrete grey model with the weighted accumulation

• Chen CI, Hsin PH, Wu CS (2010) Forecasting Taiwan’s major stock indices by the Nash nonlinear grey Bernoulli model. Expert Syst Appl 37(12):7557–7562

  Article  Google Scholar 

• Dang YG, Liu SF, Liu B (2005) The GM models that x(1)(n) be taken as initial value. Chin J Manag Sci 13(1):133–136

  Google Scholar 

• Ding S, Dang YG, Li XM, Wang JJ, Zhao K (2017) Forecasting Chinese CO₂ emissions from fuel combustion using a novel grey multivariable model. J Clean Prod 162:1527–1538

  Article  Google Scholar 

• Han Z (1995) Application of exponential cumulative generation method and logarithmic cumulative generation method to grey prediction. China J Highw Transp 8(1):52–57

  Google Scholar 

• Hu P (2016) The DGM(1,1) models that x(1)(n) be taken as initial value. Math Pract Theory 46(17):218–222

  MathSciNet  MATH  Google Scholar 

• Jiguang Sun (1987) Matrix perturbation analysis. Science Press, Beijing

  MATH  Google Scholar 

• Li C, Xie XP (2017) The DGM(1,1)atan arc-tangent function and its application. Syst Eng Theory Pract 37(12):3227–3234

  Google Scholar 

• Li GD, Yamaguchi D, Nagai M (2007) A GM(1,1)–Markov chain combined model with an application to predict the number of Chinese international airlines. Technol Forecast Soc Chang 74(8):1465–1481

  Article  Google Scholar 

• Li ML, Wang W, De G, Ji XH, Tan ZF (2018) Forecasting carbon emissions related to energy consumption in Beijing-Tianjin-Hebei region based on grey prediction theory and extreme learning machine optimized by support vector machine algorithm. Energies 11:2475–2489

  Article  Google Scholar 

• Li C, Yang YJ, Liu SF (2019a) A new method to mitigate data fluctuations for time series prediction. Appl Math Model 65:390–407

  Article  MathSciNet  Google Scholar 

• Li C, Yang YJ, Liu SF (2019b) Comparative analysis of properties of weakening buffer operators in time series prediction models. Commun Nonlinear Sci Numer Simul 68:257–285

  Article  MathSciNet  Google Scholar 

• Li SY, Yang X, Li RR (2019c) Forecasting coal consumption in India by 2030:using linear modified linear (MGM-ARIMA) and linear modified nonlinear (BP-ARIMA) combined models. Sustaina bility 11:695–713

  Article  Google Scholar 

• Lin YH, Chiu CC, Lee PC, Lin YJ (2012) Applying fuzzy grey modification model on inflow forecasting. Eng Appl Artif Intell 25(4):734–743

  Article  Google Scholar 

• Liu JF, Liu SF, Fang ZG (2016) A class of new weakening buffer operators whose adjustable intensity can be changed and their applications. Chin J Manag Sci 24(8):172–176

  Google Scholar 

• Lu JS, Xie WD, Zhou HB, Zhang AJ (2016) An optimized nonlinear grey Bernoulli model and its applications. Neurocomputing 177:206–214

  Article  Google Scholar 

• Ma YS, Dai YZ (1993) Improvement of data generation method in grey system. Syst Sci Compr Stud Agric 9(2):113–116

  Google Scholar 

• Ou SL (2012) Forecasting agricultural output with an improved grey forecasting model based on the genetic algorithm. Comput Electron Agric 85:33–39

  Article  Google Scholar 

• Qian WY, Dang YG, Wang YM (2009) GM(1, 1) model based on weighting accumulated generating operation and its application. Math Pract Theory 39(15):47–51

  Google Scholar 

• Song T (2004) The accumulated generating space. J Shandong Inst Archit Eng 19(1):88–90

  Google Scholar 

• Song ZM, Deng JL (2001) The accumulated generating operation in opposite direction and its use in grey model GOM(1,1). Syst Eng 19(1):66–69

  MathSciNet  Google Scholar 

• Song Q, Wang AM (2009) Simulation and prediction of alkalinity in sintering process based on grey least squares support vector machine. J Iron Steel Res 16(5):1–6

  Article  Google Scholar 

• Stewart GW (1977) On the perturbation of pseudo-inverses, projections and linear least squares problems. Slam Rev 19(4):634–662

  MathSciNet  MATH  Google Scholar 

• Sun X, Sun WS, Wang JZ, Zhang YX, Gao YN (2016) Using a grey-Markov model optimized by Cuckoo search algorithm to forecast the annual foreign tourist arrivals to China. Tour Manag 52:369–379

  Article  Google Scholar 

• Wang YH, Dang YG, Li YQ, Liu SF (2010) An approach to increase prediction precision of GM(1,1) model based on optimization of the initial condition. Expert Syst Appl 37(8):5640–5644

  Article  Google Scholar 

• Wang QR, Liu L, Wang S, Wang JZ, Liu M (2017) Predicting Beijing’s tertiary industry with an improved grey model. Appl Soft Comput 57:482–494

  Article  Google Scholar 

• Wu LF, Liu SF, Cui W, Liu DL, Yao TX (2014) Non-homogenous discrete grey model with fractional-order accumulation. Neural Comput Appl 25(5):1215–1221

  Article  Google Scholar 

• Wu LF, Gao XH, Xiao YL, Yang YJ, Chen XN (2018) Using a novel multi-variable grey model to forecast the electricity consumption of Shandong province in China. Energy 157:327–335

  Article  Google Scholar 

• Xu N, Dang YG (2018) Characteristic adaptive GM(1,1) model and forecasting of Chinese traffic pollution emission. Syst Eng Theory Pract 38(1):187–196

  Google Scholar 

• Yang BH, Zhang ZQ (2003) The grey model has been accumulated generating operation in reciprocal number and its application. Math Pract Theory 33(10):21–26

  Google Scholar 

• Zeng Bo (2017) Forecasting the relation of supply and demand of natural gas in China during 2015–2020 using a novel grey model. J Intell Fuzzy Syst 32(1):141–155

  Article  Google Scholar 

• Zeng B, Li C (2016) Forecasting the natural gas demand in China using a self-adapting intelligent grey model. Energy 112:810–825

  Article  Google Scholar 

• Zhao HR, Guo S (2016) An optimized grey model for annual power load forecasting. Energy 107:272–286

  Article  Google Scholar 

• Zhou WJ, Zhang HR, Dang YG, Wang ZX (2017) New information priority accumulated grey discrete model and its application. Chin J Manag Sci 25(8):140–148

  Google Scholar 

Download references