A hybrid ARIMA and support vector machines model in stock price forecasting |
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Authors: | Ping-Feng Pai Chih-Sheng Lin |
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Affiliation: | Department of Industrial Engineering and Technology Management, Da-Yeh University, 112 Shan Jiau Road, Da-Tsuen, Chung-Hwa 515, Taiwan |
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Abstract: | Traditionally, the autoregressive integrated moving average (ARIMA) model has been one of the most widely used linear models in time series forecasting. However, the ARIMA model cannot easily capture the nonlinear patterns. Support vector machines (SVMs), a novel neural network technique, have been successfully applied in solving nonlinear regression estimation problems. Therefore, this investigation proposes a hybrid methodology that exploits the unique strength of the ARIMA model and the SVMs model in forecasting stock prices problems. Real data sets of stock prices were used to examine the forecasting accuracy of the proposed model. The results of computational tests are very promising. |
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Keywords: | Artificial neural networks ARIMA Support vector machines Time series forecasting Stock prices |
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