Forecasting electricity consumption by aggregating specialized experts
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We consider the setting of sequential prediction of arbitrary sequences based on specialized experts. We first provide a review of the relevant literature and present two theoretical contributions: a general analysis of the specialist aggregation rule of Freund et al. (Proceedings of the Twenty-Ninth Annual ACM Symposium on the Theory of Computing (STOC), pp. 334–343, 1997) and an adaptation of fixed-share rules of Herbster and Warmuth (Mach. Learn. 32:151–178, 1998) in this setting. We then apply these rules to the sequential short-term (one-day-ahead) forecasting of electricity consumption; to do so, we consider two data sets, a Slovakian one and a French one, respectively concerned with hourly and half-hourly predictions. We follow a general methodology to perform the stated empirical studies and detail in particular tuning issues of the learning parameters. The introduced aggregation rules demonstrate an improved accuracy on the data sets at hand; the improvements lie in a reduced mean squared error but also in a more robust behavior with respect to large occasional errors.
KeywordsPrediction with expert advice Specialized experts Application to real data
We thank the anonymous reviewers and associated editor for their valuable comments and feedback, which improved drastically the exposition of our results and conclusions. Marie Devaine and Pierre Gaillard carried out this research while completing internships at EDF R&D, Clamart; this article is based on the technical reports (Devaine et al. 2009; Gaillard et al. 2011) subsequently written. Gilles Stoltz was partially supported by the French “Agence Nationale pour la Recherche” under grant JCJC06-137444 “From applications to theory in learning and adaptive statistics” and by the PASCAL Network of Excellence under EC grant no. 506778.
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