Conditionally Identifiable Latent Representation for Multivariate Time Series with Structural Dynamics
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arXiv
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866918406221987840 |
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| author | Chang, Minkey Kim, Jae-Young |
| author_facet | Chang, Minkey Kim, Jae-Young |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2603_22886 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Conditionally Identifiable Latent Representation for Multivariate Time Series with Structural Dynamics Chang, Minkey Kim, Jae-Young Machine Learning General Finance Statistical Finance 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. |
| title | Conditionally Identifiable Latent Representation for Multivariate Time Series with Structural Dynamics |
| topic | Machine Learning General Finance Statistical Finance |
| url | https://arxiv.org/abs/2603.22886 |