A New Hybrid Forecasting Using Decomposition Method with SARIMAX Model and Artificial Neural Network

In this paper, we present a new hybrid forecasting model using a decomposition method with SARIMAX model and Artificial Neural Network (ANN). The proposed model has combined linear and non-linear models such as a decomposition method with SARIMAX model and ANN. The new hybrid model is compared to SARIMA, SARIMAX, decomposition methods with SARIMA/SARIMAX models and ANN. We applied the new hybrid forecasting model to real monthly data sets such that the electricity consumption in the provincial area of Thailand and the SET index. The result shows that the new hybrid forecasting using a decomposition method with SARIMAX model and ANN performs well. The best hybrid model has reduced average error rate for 3 months and 12 months lead time forecasting of 47.3659% and 33.1853%, respectively. In addition, the new hybrid forecasting model between decomposition method with SARIMAX models and ANN has the lowest average MAPE of 1.9003% for 3 months and 2.2113% for 12 months lead time forecasting, respectively. The best forecasting model has been checked by using residual analysis. We conclude that the combined model is an effective way to improve more accurate forecasting than a single forecasting method.