GSpartan: a Geospatio-Temporal Multi-task Learning Framework for Multi-location Prediction

Abstract

This paper presents a novel geospatio-temporal prediction framework called GSpartan to simultaneously build local regression models at multiple locations. The framework assumes that the local models share a common, low-rank representation, which makes them amenable to multi-task learning. GSpartan learns a set of base models to capture the spatio-temporal variabilities of the data and represents each local model as a linear combination of the base models. A graph Laplacian regularization is used to enforce constraints on the local models based on their spatial autocorrelation. We also introduce sparsity-inducing norms to perform feature selection for the base models and model selection for the local models. Experimental results using historical climate data from 37 weather stations showed that, on average, GSpartan outperforms single-task learning and other existing multi-task learning methods in more than 65% of the stations, which increases to 81% when there are fewer training examples.