Comparison of time series forecasting methods using neural networks and Box-Jenkins model.

Abstract

The performance of the Box-Jenkins methods is compared with that of the neural networks in forecasting time series. Five time series of different complexities are built using back propagation neural networks were compared with the standard Box-Jenkins model. It is found that for time series with seasonal pattern, both methods produced comparable results. However, for series with irregular pattern, the Box-Jenkins outperformed the neural networks model. Results also show that neural networks are robust, provide good long-term forecasting, and represent a promising alternative method for forecasting.