A hybrid forecasting methodology using feature selection and support vector regression
Jose A. Guajardo, Jaime Miranda, Richard W. Weber · 2005
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 machines. What makes the difference in many real-world applications, however, is not the technique but an appropriated forecasting methodology. Here we present such a methodology for the regression-based forecasting approach. A hybrid system is presented that iteratively selects the most relevant features and constructs the best regression model given certain criteria. We present 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.