Time Series Forecasting with Many Predictors

TL;DR

This paper introduces GO-sdPCA, a new high-dimensional time series forecasting method that combines variable selection, peeling, and dynamic PCA to improve prediction accuracy with many predictors.

Contribution

The paper presents a novel combination of group orthogonal greedy algorithm, peeling technique, and supervised dynamic PCA for effective high-dimensional time series forecasting.

Findings

Method adapts well to unknown sparsity and factor strength.

Consistently outperforms benchmarks in economic and environmental forecasts.

Effective even with many relevant predictors relative to sample size.

Abstract

We propose a novel approach for time series forecasting with many predictors, referred to as the GO-sdPCA, in this paper. The approach employs a variable selection method known as the group orthogonal greedy algorithm and the high-dimensional Akaike information criterion to mitigate the impact of irrelevant predictors. Moreover, a novel technique, called peeling, is used to boost the variable selection procedure so that many factor-relevant predictors can be included in prediction. Finally, the supervised dynamic principal component analysis (sdPCA) method is adopted to account for the dynamic information in factor recovery. In simulation studies, we found that the proposed method adapts well to unknown degrees of sparsity and factor strength, which results in good performance even when the number of relevant predictors is large compared to the sample size. Applying to economic and environmental studies, the proposed method consistently performs well compared to some commonly used benchmarks in one-step-ahead out-sample forecasts.