Spatio-Temporal Functional Neural Networks

• URL: http://arxiv.org/abs/2009.05665v1
• Date: Fri, 11 Sep 2020 21:32:35 GMT
• Title: Spatio-Temporal Functional Neural Networks
• Authors: Aniruddha Rajendra Rao, Qiyao Wang, Haiyan Wang, Hamed Khorasgani, Chetan Gupta
• Abstract summary: We propose two novel extensions of the Neural Functional Network (FNN), a temporal regression model whose effectiveness has been proven by many researchers. The proposed models are then deployed to solve a practical and challenging precipitation prediction problem in the meteorology field.
• Score: 11.73856529960872
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Explosive growth in spatio-temporal data and its wide range of applications have attracted increasing interests of researchers in the statistical and machine learning fields. The spatio-temporal regression problem is of paramount importance from both the methodology development and real-world application perspectives. Given the observed spatially encoded time series covariates and real-valued response data samples, the goal of spatio-temporal regression is to leverage the temporal and spatial dependencies to build a mapping from covariates to response with minimized prediction error. Prior arts, including the convolutional Long Short-Term Memory (CovLSTM) and variations of the functional linear models, cannot learn the spatio-temporal information in a simple and efficient format for proper model building. In this work, we propose two novel extensions of the Functional Neural Network (FNN), a temporal regression model whose effectiveness and superior performance over alternative sequential models have been proven by many researchers. The effectiveness of the proposed spatio-temporal FNNs in handling varying spatial correlations is demonstrated in comprehensive simulation studies. The proposed models are then deployed to solve a practical and challenging precipitation prediction problem in the meteorology field.

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