Temporal Variational Implicit Neural Representations

• URL: http://arxiv.org/abs/2506.01544v1
• Date: Mon, 02 Jun 2025 11:12:30 GMT
• Title: Temporal Variational Implicit Neural Representations
• Authors: Batuhan Koyuncu, Rachael DeVries, Ole Winther, Isabel Valera,
• Abstract summary: We introduce Temporal Variational Implicit Neural Representations (TV-INRs), a probabilistic framework for modeling irregular multivariate time series.<n>TV-INRs achieves accurate individualized predictions through a single forward pass.<n>It outperforms existing methods by an order of magnitude in mean squared error for imputation task.
• Score: 15.54978130621565
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce Temporal Variational Implicit Neural Representations (TV-INRs), a probabilistic framework for modeling irregular multivariate time series that enables efficient individualized imputation and forecasting. By integrating implicit neural representations with latent variable models, TV-INRs learn distributions over time-continuous generator functions conditioned on signal-specific covariates. Unlike existing approaches that require extensive training, fine-tuning or meta-learning, our method achieves accurate individualized predictions through a single forward pass. Our experiments demonstrate that with a single TV-INRs instance, we can accurately solve diverse imputation and forecasting tasks, offering a computationally efficient and scalable solution for real-world applications. TV-INRs excel especially in low-data regimes, where it outperforms existing methods by an order of magnitude in mean squared error for imputation task.

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