Probabilistic Circuits for Irregular Multivariate Time Series Forecasting

2026-04-30

AI Summary

Existing approaches to modeling the joint probability distribution of irregular multivariate time series often struggle to simultaneously maintain model expressiveness and marginal consistency, leading to unreliable or contradictory predictions. This work proposes CircuITS, the first method to introduce probabilistic circuits into this task, constructing a structured, interpretable joint probabilistic model that supports efficient inference. By leveraging the compositional structure of probabilistic circuits, CircuITS flexibly captures complex dependencies among variables while rigorously preserving consistency between the joint distribution and its marginals. Empirical evaluation on four real-world datasets demonstrates that CircuITS significantly outperforms state-of-the-art methods in both joint and marginal density estimation.

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 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.

Problem

• irregular multivariate time series
• probabilistic forecasting
• joint probabilistic modeling
• consistent marginalization
• uncertainty quantification

Innovation

• probabilistic circuits
• irregular multivariate time series
• joint probabilistic modeling
• consistent marginalization
• time series forecasting