A model updating strategy for predicting time series with seasonal patterns
Applied Soft Computing
Linear penalization support vector machines for feature selection
PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
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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 regressionbased 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.