用概率电路建模不规则多变量时间序列，精准捕捉依赖关系并保证概率有效性。
Probabilistic Circuits for Irregular Multivariate Time Series Forecasting
- 基于概率电路架构，显式保证联合分布合法性
- 在4个真实数据集上优于现有方法的联合与边际密度估计
- 适合需要可靠不确定性量化的时间序列预测任务
联合概率建模对不规则多变量时间序列（IMTS）的准确不确定性量化至关重要。现有方法常难以在模型表达力与一致边缘化之间取得平衡，导致预测不可靠或矛盾。为此，我们提出CircuITS，一种基于概率电路的新型概率IMTS预测架构。该模型能灵活捕捉时间序列通道间的复杂依赖关系，同时结构上确保联合分布的有效性。在四个真实世界数据集上的实验表明，CircuITS在联合与边际密度估计方面均优于当前最优基线方法。
原文摘要 · Abstract (English)
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.
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