CircuITS, a novel architecture for probabilistic IMTS forecasting based on sum-product networks (SPNs), is proposed, which is flexible in capturing intricate dependencies between time series channels while structurally guaranteeing valid joint distributions.
Abstract
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 sum-product networks (SPNs). Our model is flexible in capturing intricate dependencies between time series channels while structurally guaranteeing valid joint distributions. Experiments on five real-world datasets demonstrate that CircuITS achieves superior joint and marginal density estimation compared to state-of-the-art baselines.
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