An ARIMA‐ANN Hybrid Model for Time Series Forecasting

Li Wang · Zou Haofei · Jia Su · Ling Li · Sohail S. Chaudhry

172 citations0/0 files storedSystems Research and Behavioral Science

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Forecasting Techniques and Applications Stock Market Forecasting Methods Time Series Analysis and Forecasting

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 ANN s 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.

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1971Time Series Analysis Forecasting and Control.2003Time series forecasting using a hybrid ARIMA and neural network model 1998Forecasting with artificial neural networks:1969The Combination of Forecasts 1989Combining forecasts: A review and annotated bibliography 1982The accuracy of extrapolation (time series) methods: Results of a forecasting competition 1990Combining forecasts: A review and annotated bibliography 1993Feedforward Neural Nets as Models for Time Series Forecasting

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