Deep Dynamic Probabilistic Canonical Correlation Analysis

Shiqin Tang, Shujian Yu, Yining Dong, S. Joe Qin
2/7/2025
cs.LGstat.ML

No official code found. Best implementation: marcusstang/D2PCCA (0 stars, MIT, last commit 2024-09-08).

Abstract

This paper presents Deep Dynamic Probabilistic Canonical Correlation Analysis (D2PCCA), a model that integrates deep learning with probabilistic modeling to analyze nonlinear dynamical systems. Building on the probabilistic extensions of Canonical Correlation Analysis (CCA), D2PCCA captures nonlinear latent dynamics and supports enhancements such as KL annealing for improved convergence and normalizing flows for a more flexible posterior approximation. D2PCCA naturally extends to multiple observed variables, making it a versatile tool for encoding prior knowledge about sequential datasets and providing a probabilistic understanding of the system's dynamics. Experimental validation on real financial datasets demonstrates the effectiveness of D2PCCA and its extensions in capturing latent dynamics.

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Code Implementations(1)

marcusstang/D2PCCACommunity100%
MIT

Deep Dynamic Probabilistic Canonical Correlation Analysis

00R, PythonMar 21, 20242 years agoMIT

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Cite this paper

@article{tang2025deep,
  title  = {Deep Dynamic Probabilistic Canonical Correlation Analysis},
  author = {Shiqin Tang and Shujian Yu and Yining Dong and S. Joe Qin},
  year   = {2025},
  eprint = {2502.05155},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2502.05155v1}
}

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