Temporal Multimodal Multivariate Learning
Temporal Multimodal Multivariate Learning
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DOI：
10.1145/3534678.3539159
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发表时间：
2022-06
期刊：
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通讯作者：
Hyoshin Park;Justice Darko;Niharika Deshpande;Venktesh Pandey;Hui Su;M. Ono;Dedrick Barkely;L. Folsom;D. Posselt;Steve Chien
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作者：
Hyoshin Park;Justice Darko;Niharika Deshpande;Venktesh Pandey;Hui Su;M. Ono;Dedrick Barkely;L. Folsom;D. Posselt;Steve Chien
我们引入了时间多模态多变量学习，这是一系列新的决策模型，可以从一个时间阶段到另一个时间阶段的多个峰值或多个结果变量的概率分布的同时观察中间接学习和传输在线信息。我们基于数据物理驱动的相关性，通过顺序消除不同变量和时间上的额外不确定性来近似后验，以解决不确定性下更广泛的具有挑战性的依赖时间的决策问题。对现实世界数据集（即城市交通数据和飓风集合预测数据）的广泛实验证明，所提出的目标决策在各种设置下优于最先进的基线预测方法。
We introduce temporal multimodal multivariate learning, a new family of decision making models that can indirectly learn and transfer online information from simultaneous observations of a probability distribution with more than one peak or more than one outcome variable from one time stage to another. We approximate the posterior by sequentially removing additional uncertainties across different variables and time, based on data-physics driven correlation, to address a broader class of challenging time-dependent decision-making problems under uncertainty. Extensive experiments on real-world datasets ( i.e., urban traffic data and hurricane ensemble forecasting data) demonstrate the superior performance of the proposed targeted decision-making over the state-of-the-art baseline prediction methods across various settings.