Random Forests for Time Series
Benjamin Goehry, Hui Yan, Goude, Yannig, Pascal Massart, Jean‐Michel Poggi · HAL (Le Centre pour la Communication Scientifique Directe) · 2022
Random forests are a powerful learning algorithm. However, when dealing with time series, the time-dependent structure is lost, assuming the observations are independent. We propose some variants of random forests for time series. The idea is to replace standard bootstrap with a dependent block bootstrap to subsample time series during tree construction. We present numerical experiments on electricity load forecasting. The first, at a disaggregated level and the second at a national level focusing on atypical periods. For both, we explore a heuristic for the choice of the block size. Additional experiments with generic time series data are also available.