Research on Conditionally Identifiable Latent Representation Methods for Multivariate Time Series with Structural Dynamics

The study proposes the Identifiable Variational Dynamic Factor Model (iVDFM) for multivariate time series modeling. The model combines variational inference to provide partial identifiability guarantees within a known fuzzy class range. The model's innovation terms are conditioned on observed auxiliary context and mechanism embeddings, follow a conditional exponential family prior, and are trained by maximizing the ELBO to estimate uncertainty in latent trajectories and predictions. This research has been published in the ProbML 2026 conference proceedings, and the code is open source.