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dynamic-factor-model

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chirindaopensource / identifiable_variational_dynamic_factor_model_identifiable_variational_dynamic_factor_model

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End-to-End PyTorch implementation of Chang & Kim's (2026) iVDFM pipeline, Includes: amortized encoder, RegimeNet simplex embeddings, Laplace PriorNet, diagonal AR(p) companion-form dynamics, and injective MLP decoder. Trained via ELBO with reparameterized Laplace innovations. Benchmarked against iTransformer and TimeMixer baselines.

deep-learning time-series pytorch impulse-response econometrics representation-learning variational-inference causal-inference variational-autoencoder identifiability structural-econometrics non-stationary state-space-model time-series-forecasting probabilistic-forecasting information-geometry multivariate-time-series structural-causal-model research-replication dynamic-factor-model
  • Updated Apr 25, 2026
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