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An ARIMA‐ANN Hybrid Model for Time Series Forecasting

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  • Li Wang
  • Haofei Zou
  • Jia Su
  • Ling Li
  • Sohail Chaudhry

Abstract

Autoregressive integrated moving average (ARIMA) model has been successfully applied as a popular linear model for economic time series forecasting. In addition, during the recent years, artificial neural networks (ANNs) have been used to capture the complex economic relationships with a variety of patterns as they serve as a powerful and flexible computational tool. However, most of these studies have been characterized by mixed results in terms of the effectiveness of the ANNs model compared with the ARIMA model. In this paper, we propose a hybrid model, which is distinctive in integrating the advantages of ARIMA and ANNs in modeling the linear and nonlinear behaviors in the data set. The hybrid model was tested on three sets of actual data, namely, the Wolf's sunspot data, the Canadian lynx data and the IBM stock price data. Our computational experience indicates the effectiveness of the new combinatorial model in obtaining more accurate forecasting as compared to existing models. Copyright © 2013 John Wiley & Sons, Ltd.

Suggested Citation

  • Li Wang & Haofei Zou & Jia Su & Ling Li & Sohail Chaudhry, 2013. "An ARIMA‐ANN Hybrid Model for Time Series Forecasting," Systems Research and Behavioral Science, Wiley Blackwell, vol. 30(3), pages 244-259, May.
    • Handle: RePEc:bla:srbeha:v:30:y:2013:i:3:p:244-259
    DOI: 10.1002/sres.2179
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    References listed on IDEAS

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