Robust sequential learning with applications to the forecasting of electricity consumption and of exchange rates
Apparaît également dans la collection : Thematic month on statistics - Week 1: Statistical learning / Mois thématique sur les statistiques - Semaine 1 : apprentissage
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.
![$k$-sum free sets in $[0,1]$](/media/cache/video_light/uploads/video/2015-09-10_de_Roton-video--19cf70874d3c5af7378c268cab865b06.jpg)
