Probabilistic Circuits for Irregular Multivariate Time Series Forecasting
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866909016071864320 |
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| author | Klötergens, Christian Yalavarthi, Vijaya Krishna Schmidt-Thieme, Lars |
| author_facet | Klötergens, Christian Yalavarthi, Vijaya Krishna Schmidt-Thieme, Lars |
| contents | Joint probabilistic modeling is essential for forecasting irregular multivariate time series (IMTS) to accurately quantify uncertainty. Existing approaches often struggle to balance model expressivity with consistent marginalization, frequently leading to unreliable or contradictory forecasts. To address this, we propose CircuITS, a novel architecture for probabilistic IMTS forecasting based on probabilistic circuits. Our model is flexible in capturing intricate dependencies between time series channels while structurally guaranteeing valid joint distributions. Experiments on four real world datasets demonstrate that CircuITS achieves superior joint and marginal density estimation compared to state of the art baselines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_27814 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Probabilistic Circuits for Irregular Multivariate Time Series Forecasting Klötergens, Christian Yalavarthi, Vijaya Krishna Schmidt-Thieme, Lars Machine Learning Joint probabilistic modeling is essential for forecasting irregular multivariate time series (IMTS) to accurately quantify uncertainty. Existing approaches often struggle to balance model expressivity with consistent marginalization, frequently leading to unreliable or contradictory forecasts. To address this, we propose CircuITS, a novel architecture for probabilistic IMTS forecasting based on probabilistic circuits. Our model is flexible in capturing intricate dependencies between time series channels while structurally guaranteeing valid joint distributions. Experiments on four real world datasets demonstrate that CircuITS achieves superior joint and marginal density estimation compared to state of the art baselines. |
| title | Probabilistic Circuits for Irregular Multivariate Time Series Forecasting |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2604.27814 |