Robust sequential learning with applications to the forecasting of electricity consumption and of exchange rates | Vidéo | Carmin.tv

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

By Gilles Stoltz

Appears in collections : Thematic month on statistics - Week 1: Statistical learning / Mois thématique sur les statistiques - Semaine 1 : apprentissage, Exposés de recherche

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.

Information about the video

Date of recording 04/02/2016
Date of publication 19/02/2016
Institution CIRM
Licence CC BY NC ND
Language English
Audience Researchers
Director(s) Guillaume Hennenfent
Format MP4

Citation data

DOI 10.24350/CIRM.V.18920803
Cite this video Stoltz, Gilles (04/02/2016). Robust sequential learning with applications to the forecasting of electricity consumption and of exchange rates. CIRM. Audiovisual resource. DOI: 10.24350/CIRM.V.18920803
URL https://dx.doi.org/10.24350/CIRM.V.18920803

Domain(s)

Machine Learning

Bibliography

Cesa-Bianchi, N., & Lugosi, G. (2006). Prediction, learning and games. Cambridge: Cambridge University Press - http://dx.doi.org/10.1017/CBO9780511546921

MSC codes

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