Random Forests for Time Series

• Main Author: Goehry, Benjamin
• Publication Date: 2023
• Other Authors: Yan , Hui, Goude , Yannig, Massart , Pascal, Poggi , Jean-Michel
• Format: Article
• Language: eng
• Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
• Download full: https://doi.org/10.57805/revstat.v21i2.400
• topic: Block bootstrap; Random forests; Regression; Time series

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.