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Erschienen in:
2006 | OriginalPaper | Buchkapitel
A Machine Learning Approach to Define Weights for Linear Combination of Forecasts
verfasst von : Ricardo Prudêncio, Teresa Ludermir
Erschienen in: Artificial Neural Networks – ICANN 2006
Verlag: Springer Berlin Heidelberg
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The linear combination of forecasts is a procedure that has improved the forecasting accuracy for different time series. In this procedure, each method being combined is associated to a numerical weight that indicates the contribution of the method in the combined forecast. We present the use of machine learning techniques to define the weights for the linear combination of forecasts. In this paper, a machine learning technique uses features of the series at hand to define the adequate weights for a pre-defined number of forecasting methods. In order to evaluate this solution, we implemented a prototype that uses a MLP network to combine two widespread methods. The experiments performed revealed significantly accurate forecasts.