AI RESEARCH
Conditionally Identifiable Latent Representation for Multivariate Time Series with Structural Dynamics
arXiv CS.LG
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ArXi:2603.22886v1 Announce Type: new 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 Krylo methods.