Supervised Dynamic Dimension Reduction with Deep Neural Network

• URL: http://arxiv.org/abs/2508.03546v2
• Date: Wed, 06 Aug 2025 02:41:26 GMT
• Title: Supervised Dynamic Dimension Reduction with Deep Neural Network
• Authors: Zhanye Luo, Yuefeng Han, Xiufan Yu,
• Abstract summary: We propose a novel Supervised Deep Dynamic Principal component analysis framework.<n>We construct target-aware predictors by scaling the original predictors in a supervised manner.<n>A principal component analysis is then performed on the target-aware predictors to extract the estimated SDDP factors.
• Score: 3.0040661953201475
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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