China’s energy consumption forecasting by GMDH based auto-regressive 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.