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. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Prudêncio, R., & Ludermir, T. (2006). A machine learning approach to define weights for linear combination of forecasts. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4131 LNCS-I, pp. 274–283). Springer Verlag. https://doi.org/10.1007/11840817_29
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