A Machine Learning Approach to Define Weights for Linear Combination of Forecasts

  • Ricardo Prudêncio
  • Teresa Ludermir
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4131)


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.


Forecast Error Forecast Method Machine Learn Approach Forecast Period Time Series Forecast 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ricardo Prudêncio
    • 1
  • Teresa Ludermir
    • 2
  1. 1.Departament of Information ScienceFederal University of PernambucoRecifeBrazil
  2. 2.Center of InformaticsFederal University of PernambucoRecifeBrazil

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