Journal
JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS
Volume 31, Issue 1-3, Pages 69-89Publisher
KLUWER ACADEMIC PUBL
DOI: 10.1023/A:1012061814242
Keywords
Bayesian combined predictor; time series prediction; local predictors; short-term load forecasting
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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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