AI RESEARCH
Probabilistic Circuits for Irregular Multivariate Time Series Forecasting
arXiv CS.LG
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ArXi:2604.27814v1 Announce Type: new 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.