# Supervised Dynamic Dimension Reduction with Deep Neural Network

arXiv: https://arxiv.org/abs/2508.03546 · HTML: https://mlsanity.com/paper/2508.03546/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 62 references that are in the map.
- First posted on arXiv 5 Aug 2025.

## Builds on (most cited first, 19 of 19)

- [Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting](https://mlsanity.com/paper/2106.13008/) (2021)
- [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018)
- [TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis](https://mlsanity.com/paper/2210.02186/) (2022)
- [DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks](https://mlsanity.com/paper/1704.04110/) (2017)
- [Think Globally, Act Locally: A Deep Neural Network Approach to High-Dimensional Time Series Forecasting](https://mlsanity.com/paper/1905.03806/) (2019)
- [A survey of dimensionality reduction techniques](https://mlsanity.com/paper/1403.2877/) (2014)
- [Factor Augmented Sparse Throughput Deep ReLU Neural Networks for High Dimensional Regression](https://mlsanity.com/paper/2210.02002/) (2022)
- [Estimating Number of Factors by Adjusted Eigenvalues Thresholding](https://mlsanity.com/paper/1909.10710/) (2019)
- [Sufficient Forecasting Using Factor Models](https://mlsanity.com/paper/1505.07414/) (2015)
- [CP Factor Model for Dynamic Tensors](https://mlsanity.com/paper/2110.15517/) (2021)
- [Inverse Moment Methods for Sufficient Forecasting using High-Dimensional Predictors](https://mlsanity.com/paper/1705.00395/) (2017)
- [Tensor Factor Model Estimation by Iterative Projection](https://mlsanity.com/paper/2006.02611/) (2020)
- [Rank Determination in Tensor Factor Model](https://mlsanity.com/paper/2011.07131/) (2020)
- [Tensor Principal Component Analysis in High Dimensional CP Models](https://mlsanity.com/paper/2108.04428/) (2021)
- [Factor Augmented Tensor-on-Tensor Neural Networks](https://mlsanity.com/paper/2405.19610/) (2024)
- [Simultaneous Decorrelation of Matrix Time Series](https://mlsanity.com/paper/2103.09411/) (2021)
- [Estimation and Inference for CP Tensor Factor Models](https://mlsanity.com/paper/2406.17278/) (2024)
- [Belted and Ensembled Neural Network for Linear and Nonlinear Sufficient Dimension Reduction](https://mlsanity.com/paper/2412.08961/) (2024)
- [Supervised Dynamic PCA: Linear Dynamic Forecasting with Many Predictors](https://mlsanity.com/paper/2307.07689/) (2023)

## Built on by (most cited first, 1 of 1)

- [Factor Augmented High-Dimensional SGD](https://mlsanity.com/paper/2605.19291/) (2026)

## Abstract (arXiv)

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.