Temporal Multimodal Multivariate Learning
This addresses challenging decision-making problems in domains like urban traffic and weather forecasting, though it appears incremental as an extension of existing multimodal/multivariate approaches.
The paper tackles time-dependent decision-making problems under uncertainty by introducing temporal multimodal multivariate learning models that transfer online information across time stages. Experiments on urban traffic and hurricane forecasting datasets show superior performance over state-of-the-art baseline methods.
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.