Robust sequential learning with applications to the forecasting of air quality and of exchange rates

Abstract

This paper is an extended written version of the talk I delivered at the “XLe Journées de Statistique” in Ottawa, 2004, when being awarded the Marie-Jeanne Laurent-Duhamel prize. It is devoted to surveying some fundamental as well as some more recent results in the field of sequential prediction of individual sequences with expert advice. It then performs two empirical studies following the stated general methodology: the first one to air-quality forecasting and the second one to the prediction of electricity consumption. Most results mentioned in the paper are based on joint works with Yannig Goude (EDF R&D) and Vivien Mallet (INRIA), together with some students whom we co-supervised for their M.Sc. theses: Marie Devaine, Sébastien Gerchinovitz and Boris Mauricette. Classification AMS 2000 : primaire 62-02, 62L99, 62P12, 62P30 Mots-clés : Agrégation séquentielle, prévision avec experts, suites individuelles, prévision de la qualité de l’air, prévision de la consommation électrique Keywords: Sequential aggregation of predictors, prediction