Combining neural network model with seasonal time series ARIMA model

This paper proposes a hybrid forecasting model, which combines the seasonal time series ARIMA (SARIMA) and the neural network back propagation (BP) models, known as SARIMABP. This model was used to forecast two seasonal time series data of total production value for Taiwan machinery industry and the soft drink time series. The forecasting performance was compared among four models, i.e., the SARIMABP and SARIMA models and the two neural network models with differenced and deseasonalized data, respectively. Among these methods, the mean square error (MSE), the mean absolute error (MAE), and the mean absolute percentage error (MAPE) of the SARIMABP model were the lowest. The SARIMABP model was also able to forecast certain significant turning points of the test time series. (C) 2002 Elsevier Science Inc. All rights reserved.