Supervised Dynamic Dimension Reduction with Deep Neural Network

TL;DR

This paper introduces a supervised deep neural network framework for dynamic dimension reduction tailored to time series forecasting, improving accuracy and interpretability by incorporating target information into factor extraction.

Contribution

The paper proposes a novel SDDP method that integrates target-aware predictors with neural networks for improved dimension reduction and forecasting accuracy.

Findings

Achieves significant forecasting accuracy improvements over existing methods.

Produces more interpretable and target-specific latent factors.

Demonstrates effectiveness on real-world datasets.

Abstract

This paper studies the problem of dimension reduction, tailored to improving time series forecasting with high-dimensional predictors. We propose a novel Supervised Deep Dynamic Principal component analysis (SDDP) framework that incorporates the target variable and lagged observations into the factor extraction process. Assisted by a temporal neural network, we construct target-aware predictors by scaling the original predictors in a supervised manner, with larger weights assigned to predictors with stronger forecasting power. A principal component analysis is then performed on the target-aware predictors to extract the estimated SDDP factors. This supervised factor extraction not only improves predictive accuracy in the downstream forecasting task but also yields more interpretable and target-specific latent factors. Building upon SDDP, we propose a factor-augmented nonlinear dynamic forecasting model that unifies a broad family of factor-model-based forecasting approaches. To further demonstrate the broader applicability of SDDP, we extend our studies to a more challenging scenario when the predictors are only partially observable. We validate the empirical performance of the proposed method on several real-world public datasets. The results show that our algorithm achieves notable improvements in forecasting accuracy compared to state-of-the-art methods.