概率电路用于不规则多变量时间序列预测
Probabilistic Circuits for Irregular Multivariate Time Series Forecasting
AI总结：
本文提出CircuITS概率电路架构，有效捕捉多变量时间序列复杂依赖关系，提升不规则时间序列联合分布估计精度。
摘要：
联合概率建模对准确量化不规则多变量时间序列（IMTS）的不确定性至关重要。现有方法常难以平衡模型表达能力和一致边缘化，导致不可靠或矛盾的预测。为此，我们提出CircuITS，一种基于概率电路的概率IMTS预测架构。我们的模型在捕捉时间序列通道间的复杂依赖关系方面具有灵活性，同时在结构上保证有效的联合分布。在四个真实世界数据集上的实验表明，CircuITS在联合和边缘密度估计方面优于现有最先进基线。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
