Hybridization of intelligent techniques and ARIMA models for time series prediction

Traditionally, the autoregressivemoving average (ARMA)model has been one of themost widely used linear models in time series prediction. Recent research activities in forecasting with artificial neural networks (ANNs) suggest that ANNs can be a promising alternative to the traditional ARMA structure. These linear models and ANNs are often compared with mixed conclusions in terms of the superiority in forecasting performance. In this paper we propose a hybridization of intelligent techniques such asANNs, fuzzy systems and evolutionary algorithms, so that the final hybrid ARIMA–ANN model could outperform the prediction accuracy of those models when used separately. More specifically, we propose the use of fuzzy rules to elicit the order of theARMA orARIMA model, without the intervention of a human expert, and the use of a hybrid ARIMA–ANN model that combines the advantages of the easy-to-use and relatively easy-to-tune ARIMA models, and the computational power of ANNs. © 2007 Elsevier B.V. All rights reserved.