Temporal Multimodal Multivariate Learning
Abstract
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.
Cite
@article{arxiv.2206.06878,
title = {Temporal Multimodal Multivariate Learning},
author = {Hyoshin Park and Justice Darko and Niharika Deshpande and Venktesh Pandey and Hui Su and Masahiro Ono and Dedrick Barkely and Larkin Folsom and Derek Posselt and Steve Chien},
journal= {arXiv preprint arXiv:2206.06878},
year = {2022}
}
Comments
11 pages, 12 figures, SIGKDD Conference on Knowledge Discovery and Data Mining,