研究人员推出了一种用于预测不规则多元时间序列的新架构 CircuITS，该架构利用概率电路。该方法旨在通过更好地平衡模型表达能力与一致的边际化来提高不确定性量化的准确性。在真实数据集上的实验表明，CircuITS 在联合和边际密度估计方面优于现有的最先进方法。 AI
影响 引入了一种新颖的时间序列预测架构，可能会提高复杂数据集中的不确定性量化。
排序理由 在 arXiv 上发表的学术论文，详细介绍了一种新的预测架构。
AI 生成摘要 · Google Gemini · 来自 3 个来源。 我们如何撰写摘要 →
arXiv:2604.27814v1 Announce Type: new 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…
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 contradict…
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 contradict…