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This paper proposes a general Multiple-Model Predictor frame-work for forecasting time series that presents multiple patterns (behaviours). The proposed approach allows off-line identifying the different time-series behaviours, training a model for each behaviour and identifing on-line which is the pattern and associated model to use at each time instant. A particular case based on K-means and Radial Basis Functions (RBF) is proposed to exemplify a possible implementation. To illustrate the use and performance of the proposed approach, an application of the short-term water demand forecast is presented using demand data from the Barcelona drinking water network.
CitationLópez, R., Puig, V. A Multiple-model predictor approach based on an On-line mode recognition with application to water demand forecasting. A: International Work-conference on Time Series. "ITISE 2015 - 1st International work-conference on Time Series, Granada (Spain), 1-3 July 2015". Granada: Universidad de Granada, 2015, p. 895-907.
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