GMDH based auto-regressive model for China's energy consumption prediction

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 study 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 GAR models, AS-GAR, MR-GAR, SRMSE-GAR and SMAPE-GAR, are constructed according to different external criteria. We 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, SVM regression model and GM (1, 1) model. Finally, we give the out of sample prediction from 2014 to 2020 by GAR model.