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Agrégation séquentielle de prédicteurs : méthodologie générale et applications à la prévision de la qualité de l'air et à celle de la consommation électrique

Author

Listed:
  • Gilles Stoltz

    (GREGH - Groupement de Recherche et d'Etudes en Gestion à HEC - HEC Paris - Ecole des Hautes Etudes Commerciales - CNRS - Centre National de la Recherche Scientifique)

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.

Suggested Citation

  • Gilles Stoltz, 2010. "Agrégation séquentielle de prédicteurs : méthodologie générale et applications à la prévision de la qualité de l'air et à celle de la consommation électrique," Post-Print hal-00637060, HAL.
    • Handle: RePEc:hal:journl:hal-00637060
    as

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    Cited by:

    1. Alquier Pierre & Li Xiaoyin & Wintenberger Olivier, 2014. "Prediction of time series by statistical learning: general losses and fast rates," Dependence Modeling, De Gruyter, vol. 1(2013), pages 65-93, January.
    2. Jeremy Fouliard & Michael Howell & Hélène Rey & Vania Stavrakeva, 2020. "Answering the Queen: Machine Learning and Financial Crises," NBER Working Papers 28302, National Bureau of Economic Research, Inc.
    3. Amat, Christophe & Michalski, Tomasz & Stoltz, Gilles, 2018. "Fundamentals and exchange rate forecastability with simple machine learning methods," Journal of International Money and Finance, Elsevier, vol. 88(C), pages 1-24.
    4. Vincent Margot & Christophe Geissler & Carmine de Franco & Bruno Monnier, 2021. "ESG Investments: Filtering versus Machine Learning Approaches," Applied Economics and Finance, Redfame publishing, vol. 8(2), pages 1-16, March.
    5. Michaël Zamo & Liliane Bel & Olivier Mestre, 2021. "Sequential aggregation of probabilistic forecasts—Application to wind speed ensemble forecasts," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(1), pages 202-225, January.

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