Random Forests for Time Series

Benjamin Goehry (1, 2) , Hui Yan (3) , Yannig Goude (3, 1) , Pascal Massart (1, 2) , Jean-Michel Poggi (1, 4, 2)

1 Université Paris-Saclay
2 CELESTE - Statistique mathématique et apprentissage
3 EDF - EDF
4 UPCité - Université Paris Cité

Random forests were introduced in 2001 by Breiman and have since become a popular learning algorithm, for both regression and classification. However, when dealing with time series, random forests do not integrate the time-dependent structure, implicitly supposing that the observations are independent. We propose some variants of the random forests designed for time series. The idea is to replace the standard bootstrap with a dependent bootstrap (i.e block bootstrap) to subsample time series during the tree construction phase to take time dependence into account. We then present two numerical experiments on electricity load forecasting. The first one, at a disaggregated level, is based on an application to load forecasting of a building and illustrate how the variants may perform. The second one is at a more aggregated level (French national forecasting) but focusing on atypical periods. For both, we explore a heuristic for the choice of the block size, the new parameter.

Mots clés: Block bootstrap; Random forests; Time series

Domaines: Machine Learning [stat.ML]

Benjamin Goehry, Hui Yan, Yannig Goude, Pascal Massart, Jean-Michel Poggi. Random Forests for Time Series. 2021. ⟨hal-03129751⟩