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A Bayesian Multiple Models Combination Method for Time Series Prediction

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Abstract

In this paper we present the Bayesian Combined Predictor (BCP), a probabilistically motivated predictor for time series prediction. BCP utilizes local predictors of several types (e.g., linear predictors, artificial neural network predictors, polynomial predictors etc.) and produces a final prediction which is a weighted combination of the local predictions; the weights can be interpreted as Bayesian posterior probabilities and are computed online. Two examples of the method are given, based on real world data: (a) short term load forecasting for the Greek Public Power Corporation dispatching center of the island of Crete, and (b) prediction of sugar beet yield based on data collected from the Greek Sugar Industry. In both cases, the BCP outperforms conventional predictors.

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Authors and Affiliations

  1. Faculty of Engineering, Aristotle University, Thessaloniki, GR, 54006, Greece

    V. Petridis, A. Kehagias, L. Petrou, A. Bakirtzis, S. Kiartzis & H. Panagiotou

  2. Greek Sugar Industry, Thessaloniki, Greece

    N. Maslaris

Authors
  1. V. Petridis
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  2. A. Kehagias
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  3. L. Petrou
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  4. A. Bakirtzis
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  5. S. Kiartzis
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  6. H. Panagiotou
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  7. N. Maslaris
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Petridis, V., Kehagias, A., Petrou, L. et al. A Bayesian Multiple Models Combination Method for Time Series Prediction. Journal of Intelligent and Robotic Systems 31, 69–89 (2001). https://doi.org/10.1023/A:1012061814242

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