A Forecasting Methodology Using Support Vector Regression and Dynamic Feature Selection

José Guajardo (), Richard Weber () and Jaime Miranda ()
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José Guajardo: Department of Industrial Engineering, University of Chile, Chile
Richard Weber: Department of Industrial Engineering, University of Chile, Chile
Jaime Miranda: Department of Industrial Engineering, University Diego Portales, Chile

Journal of Information & Knowledge Management (JIKM), 2006, vol. 05, issue 04, 329-335

Abstract: Various techniques have been proposed to forecast a given time series. Models from the ARIMA family have been successfully used, as well as regression approaches based on e.g. linear, non-linear regression, neural networks, and Support Vector Regression. What makes the difference in many real-world applications, however, is not the technique but an appropriate forecasting methodology. Here, we propose such a methodology for the regression-based forecasting approach. A hybrid system is presented that iteratively selects the most relevant features and constructs the regression model optimizing its parameters dynamically. We develop a particular technique for feature selection as well as for model construction. The methodology, however, is a generic one providing the opportunity to employ alternative approaches within our framework. The application to several time series underlines its usefulness.

Keywords: Support vector regression; time series forecasting; feature selection (search for similar items in EconPapers)
Date: 2006
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Citations: View citations in EconPapers (1)

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DOI: 10.1142/S021964920600158X

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