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Time Series Forecasting with Many Predictors
Version 1
: Received: 13 June 2024 / Approved: 21 June 2024 / Online: 21 June 2024 (15:12:24 CEST)
A peer-reviewed article of this Preprint also exists.
Huang, S.-C.; Tsay, R.S. Time Series Forecasting with Many Predictors. Mathematics 2024, 12, 2336. Huang, S.-C.; Tsay, R.S. Time Series Forecasting with Many Predictors. Mathematics 2024, 12, 2336.
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.
Keywords
Supervided Principal Component Analysis; Diffusion index; Lasso; Dynamic dependence
Subject
Business, Economics and Management, Econometrics and Statistics
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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