Probabilistic Circuits for Irregular Multivariate Time Series Forecasting
For practitioners needing reliable uncertainty quantification in irregular multivariate time series, CircuITS provides a model that balances expressivity with guaranteed valid joint distributions.
CircuITS uses probabilistic circuits for joint probabilistic modeling of irregular multivariate time series, achieving superior density estimation over state-of-the-art baselines on four real-world datasets.
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.