A Supervised Screening and Regularized Factor-Based Method for Time Series Forecasting

TL;DR

This paper proposes a novel supervised screening and regularized factor-based framework for time series forecasting that improves prediction accuracy in high-dimensional settings by combining static and dynamic predictor selection with PCA and LASSO.

Contribution

It introduces the SSRF framework that systematically integrates predictor screening, scaling, PCA, and regularization for enhanced high-dimensional time series forecasting.

Findings

SSRF outperforms traditional methods in simulations.

Parameter tuning strategies improve model performance.

Empirical analysis shows SSRF's superior out-of-sample forecasts in Chinese macroeconomic data.

Abstract

Factor-based forecasting using Principal Component Analysis (PCA) is an effective machine learning tool for dimension reduction with many applications in statistics, economics, and finance. This paper introduces a Supervised Screening and Regularized Factor-based (SSRF) framework that systematically addresses high-dimensional predictor sets through a structured four-step procedure integrating both static and dynamic forecasting mechanisms. The static approach selects predictors via marginal correlation screening and scales them using univariate predictive slopes, while the dynamic method screens and scales predictors based on time series regression incorporating lagged predictors. PCA then extracts latent factors from the scaled predictors, followed by LASSO regularization to refine predictive accuracy. In the simulation study, we validate the effectiveness of SSRF and identify its parameter adjustment strategies in high-dimensional data settings. An empirical analysis of macroeconomic indices in China demonstrates that the SSRF method generally outperforms several commonly used forecasting techniques in out-of-sample predictions.