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arXiv · 2603.22886

Conditionally Identifiable Latent Representation for Multivariate Time Series with Structural Dynamics

Minkey Chang·Jae-Young Kim
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Abstract

We propose the Identifiable Variational Dynamic Factor Model (iVDFM), which learns latent factors from multivariate time series with identifiability guarantees. By applying iVAE-style conditioning to the innovation process driving the dynamics rather than to the latent states, we show that factors are identifiable up to permutation and component-wise affine (or monotone invertible) transformations. Linear diagonal dynamics preserve this identifiability and admit scalable computation via companion-matrix and Krylov methods. We demonstrate improved factor recovery on synthetic data, stable intervention accuracy on synthetic SCMs, and competitive probabilistic forecasting on real-world benchmarks.

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Minkey Chang, Jae-Young Kim. 2026-03-24. Conditionally Identifiable Latent Representation for Multivariate Time Series with Structural Dynamics. https://arxiv.org/abs/2603.22886

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