China's Energy Consumption Forecasting by GMDH Based Auto-Regressive Model  

作　　者：XIE Ling XIAO Jin HU Yi ZHAO Hengjun XIAO Yi 

机构地区：[1]Business School, Sichuan University [2]School of Economics and Management, University of Chinese Academy of Sciences [3]School of Economics and Management, Sichuan Radio and TV University [4]School of Information Management, Central China Normal University

出　　处：《Journal of Systems Science & Complexity》2017年第6期1332-1349,共18页系统科学与复杂性学报（英文版）

基　　金：partly supported by the Natural Science Foundation of China under Grant Nos.71471124and 71301160;the National Social Science Foundation of China under Grant No.14BGL175;Youth Foundation of Sichuan Province under Grant No.2015RZ0056;Sichuan Province Social Science Planning Project under Grant No.SC14C019;Excellent Youth Fund of Sichuan University under Grant Nos.skqx201607 and skzx2016-rcrw14;Young Teachers Visiting Scholar Program of Sichuan University;Soft Science Foundation of Chengdu Technology Bureau under Grant No.2015-RK00-00259-ZF;Teaching Reform Project of Sichuan Radio and TV University under Grant No.XMZSXX2016003Z

摘　　要：It is very significant for us to predict future energy consumption accurately. As for China's energy consumption annual time series, the sample size is relatively small. This paper combines the traditional auto-regressive model with group method of data handling(GMDH) suitable for small sample prediction, and proposes a novel GMDH based auto-regressive(GAR) model. This model can finish the modeling process in self-organized manner, including finding the optimal complexity model, determining the optimal auto-regressive order and estimating model parameters. Further, four different external criteria are proposed and the corresponding four GAR models are constructed. The authors conduct empirical analysis on three energy consumption time series, including the total energy consumption, the total petroleum consumption and the total gas consumption. The results show that AS-GAR model has the best forecasting performance among the four GAR models, and it outperforms ARIMA model, BP neural network model, support vector regression model and GM(1, 1) model.Finally, the authors give the out of sample prediction of China's energy consumption from 2014 to 2020 by AS-GAR model.It is very significant for us to predict future energy consumption accurately. As for China＇s energy consumption annual time series, the sample size is relatively small. This paper combines the traditional auto-regressive model with group method of data handling（GMDH） suitable for small sample prediction, and proposes a novel GMDH based auto-regressive（GAR） model. This model can finish the modeling process in self-organized manner, including finding the optimal complexity model, determining the optimal auto-regressive order and estimating model parameters. Further, four different external criteria are proposed and the corresponding four GAR models are constructed. The authors conduct empirical analysis on three energy consumption time series, including the total energy consumption, the total petroleum consumption and the total gas consumption. The results show that AS-GAR model has the best forecasting performance among the four GAR models, and it outperforms ARIMA model, BP neural network model, support vector regression model and GM（1, 1） model.Finally, the authors give the out of sample prediction of China＇s energy consumption from 2014 to 2020 by AS-GAR model.

关 键 词：Auto-regressive model energy demand prediction GMDH small sample forecasting 

分 类 号：O212.1[理学—概率论与数理统计]