arXiv Machine Learning July 8, 2026 By Batuhan Koyuncu, Rachael DeVries, Ole Winther, Isabel Valera

Temporal Variational Implicit Neural Representations

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arXiv:2506. 01544v2 Announce Type: replace Abstract: We introduce Temporal Variational Implicit Neural Representations (TV-INRs), a probabilistic framework for modeling irregular multivariate time series that enables efficient and accurate individualized imputation and forecasting.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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Related stories

TS-ICL: A Flexible Time-Indexed Foundation Model for Time Series via In-Context Learning

TANDEM: Temporal Attention-guided Neural Differential Equations for Missingness in Time Series Classification

ALER-TI: Aligned Latent Embedding Retrieval for Time Series Imputation

PAMF: Prior-Aware Multimodal Fusion for Incomplete Time Series Data

FADTI: Fourier and Attention Driven Diffusion for Multivariate Time Series Imputation

Towards a Unified Generative Model for Scarce Time Series with Domain Experts