Variable Selection in Time Series Forecasting Using Random Forests

TLDR

Machine learning, especially random forests, has become popular for time‑series forecasting, yet its forecasting properties remain largely unexplored. This study evaluates random forests for one‑step forecasting on two large short‑time‑series datasets to identify an optimal set of predictor variables. We applied random forests to 16,000 simulated ARFIMA series and 135 mean annual temperature series, comparing performance against benchmark methods. Random forests achieve best performance with a small number of recent lagged predictors, suggesting that fewer variables can yield higher predictive accuracy.

Abstract

Time series forecasting using machine learning algorithms has gained popularity recently. Random forest is a machine learning algorithm implemented in time series forecasting; however, most of its forecasting properties have remained unexplored. Here we focus on assessing the performance of random forests in one-step forecasting using two large datasets of short time series with the aim to suggest an optimal set of predictor variables. Furthermore, we compare its performance to benchmarking methods. The first dataset is composed by 16,000 simulated time series from a variety of Autoregressive Fractionally Integrated Moving Average (ARFIMA) models. The second dataset consists of 135 mean annual temperature time series. The highest predictive performance of RF is observed when using a low number of recent lagged predictor variables. This outcome could be useful in relevant future applications, with the prospect to achieve higher predictive accuracy.