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Robust sequential learning with applications to the forecasting of electricity consumption and of exchange rates

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Authors : Stoltz, Gilles (Author of the conference)
CIRM (Publisher )

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Abstract : Sometimes, you feel you're spoilt for choice: there are so many good predictors that you could use! Why select and focus on just one? I will review the framework of robust online aggregation (also known as prediction of individual sequences or online aggregation of expert advice). This setting explains how to combine base forecasts provided by ensemble methods. No stochastic modeling is needed and the performance achieved is comparable to the one of the best (constant convex combination of) base forecast(s). I will illustrate the technology on various data sets, including electricity consumption and exchange rates. More importantly, I will point out open issues, both on the theoretical and on the practical sides.

MSC Codes :
62Lxx - Sequential methods
62P12 - Applications of statistics to environnemental and related topics
62P20 - Applications of statistics to economics

    Information on the Video

    Film maker : Hennenfent, Guillaume
    Language : English
    Available date : 19/02/16
    Conference Date : 04/02/16
    Subseries : Research talks
    arXiv category : Machine Learning
    Mathematical Area(s) : Probability & Statistics
    Format : MP4 (.mp4) - HD
    Video Time : 00:45:20
    Targeted Audience : Researchers
    Download : https://videos.cirm-math.fr/2016-02-04_Stoltz.mp4

Information on the Event

Event Title : Thematic month on statistics - Week 1: Statistical learning / Mois thématique sur les statistiques - Semaine 1 : apprentissage
Event Organizers : Ghattas, Badih ; Ralaivola, Liva
Dates : 01/02/16 - 05/02/16
Event Year : 2016
Event URL : http://conferences.cirm-math.fr/1615.html

Citation Data

DOI : 10.24350/CIRM.V.18920803
Cite this video as: Stoltz, Gilles (2016). Robust sequential learning with applications to the forecasting of electricity consumption and of exchange rates. CIRM. Audiovisual resource. doi:10.24350/CIRM.V.18920803
URI : http://dx.doi.org/10.24350/CIRM.V.18920803

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