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Deep Dynamic Probabilistic Canonical Correlation Analysis
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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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InfoDPCCA: Information-Theoretic Dynamic Probabilistic Canonical Correlation Analysis
InfoDPCCA combines a dynamic probabilistic CCA model with an information-bottleneck objective so the shared latent state is trained to contain only the mutual information of the two sequences and still predict the nex...
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