A hybrid model for time series forecasting

Abstract

For time series, the problem that we often encounter is how to extract the patterns hidden in the real world data for forecasting its future values. A single linear or nonlinear model is inadequate in modeling and forecasting the time series, because most of them usually contain both linear and nonlinear patterns. This study constructs a hybrid forecasting model that combines autoregressive integrated moving average (ARIMA) with Elman artificial neural network (ANN) for short-term forecasting of time series. The proposed approach considers the linear and nonlinear patterns in the real data simultaneously so that it can mine more precise characteristics to describe the time series better. Finally, the forecasting results of the hybrid model are adjusted with the knowledge from text mining and expert system. The empirical results on the container throughput forecast of Tianjin Port show that the forecasts by the hybrid model are superior to those of ARIMA model and Elman network.