Forecasting China's Foreign Trade Volume Based on a New Hybrid Model

摘要

The autoregressive integrated moving average (ARIMA) model has been one of the most widely used linear models in time series forecasting. Recent researches suggest that artificial neural networks can improve the traditional linear methods. Therefore, this paper combines a hybrid ARIMA and Elman's recurrent neural networks (ERNN) model to forecast foreign trade time series. The results show that the proposed model has more forecasting accuracy than that of single model.