Spatiotemporal analyses can enable many discoveries including reducing traffic congestion, identifying hotspot areas to deploy mobile clinics, and urban planning. Unfortunately, the data poses many computational challenges. Standard assumptions in machine learning and data mining algorithms are violated by the complex nature of spatiotemporal data. These include spatial and temporal correlation of observations, dynamic and abrupt changes in observations, variability in measurements with respect to length and frequency, and multi-sourced data that spans multiple sources of information. In recognition of these challenges, various efforts have been undertaken to develop specialized spatiotemporal models. Yet, to date, these algorithms are predominately designed to analyze small- to medium-sized datasets. The goal of this project is to develop a comprehensive computational tensor platform to perform automated, data-driven discovery from spatiotemporal data across a broad range of applications.

This is an ongoing collaboration with Li Xiong and Jimeng Sun. The project is supported by the National Science Foundation (NSF) under award number 1838200.

TIMES: The tensor-based spatiotemporal framework