Using and combining predictors that specialize
STOC '97 Proceedings of the twenty-ninth annual ACM symposium on Theory of computing
Machine Learning - Special issue on context sensitivity and concept drift
Potential-Based Algorithms in On-Line Prediction and Game Theory
Machine Learning
On the Competitive Theory and Practice of Portfolio Selection (Extended Abstract)
LATIN '00 Proceedings of the 4th Latin American Symposium on Theoretical Informatics
Internal Regret in On-Line Portfolio Selection
Machine Learning
Prediction, Learning, and Games
Prediction, Learning, and Games
Generalized Additive Models (Texts in Statistical Science)
Generalized Additive Models (Texts in Statistical Science)
Improved second-order bounds for prediction with expert advice
Machine Learning
From External to Internal Regret
The Journal of Machine Learning Research
Prediction with expert advice for the Brier game
Proceedings of the 25th international conference on Machine learning
Statistical Analysis and Data Mining
Optimized renewable energy forecasting in local distribution networks
Proceedings of the Joint EDBT/ICDT 2013 Workshops
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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.