A quantitative and qualitative analysis of tensor decompositions on spatiotemporal data

Abstract

With the recent explosion of systems capable of generating and storing large quantities of GPS data, there is an opportunity to develop novel techniques for analyzing and gaining meaningful insights into this spatiotemporal data. In this paper we examine the application of tensor decompositions, a high-dimensional data analysis technique, to georeferenced data sets. Guidance is provided on fitting spatiotemporal data into the tensor model and analyzing the results. We find that tensor decompositions provide insight and that future research into spatiotemporal tensor decompositions for pattern detection, clustering, and anomaly detection is warranted.