An ARIMA‐ANN Hybrid Model for Time Series Forecasting. (1st May 2013)

• Record Type: Journal Article
• Authors: Wang, Li; Zou, Haofei; Su, Jia; Li, Ling; Chaudhry, Sohail
• Other Names: Xu Li Da guestEditor.
• Is Part Of: Systems research and behavioral science. Volume 30:Number 3(2013:May/Jun.)
• Page Start: 244
• Page End: 259
• Publication Date: 2013-05-01
• Subjects: ARIMA -- Box–Jenkins methodology -- artificial neural networks -- time series forecasting -- combined forecast -- systems science; Psychology -- Periodicals; Social sciences -- Periodicals; System theory -- Periodicals; 003
• DOI: 10.1002/sres.2179
• Languages: English
• ISSNs: 1092-7026

Abstract

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.