Forthcoming

Random Forests for Time Series

Accepted - November 2021

Authors

Keywords:

Block bootstrap, Random forests, Regression, Time series

Abstract

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.

Published

2021-11-10

How to Cite

Goehry, B., Yan , H., Goude , Y., Massart , P., & Poggi , J.-M. (2021). Random Forests for Time Series: Accepted - November 2021. REVSTAT-Statistical Journal. Retrieved from https://revstat.ine.pt/index.php/REVSTAT/article/view/400

Issue

Section

Forthcoming Paper

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