Statistical Learning and Decision Making for Spatio-Temporal Data

Spatio-temporal data modeling and sequential decision analytics are a growing area of research, with an enormous amount of modern spatio-temporal data being consistently collected from the real world. These data include power outages, police 911 calls, healthcare records, credit card transactions, social media posts, etc. Understanding the intricate spatio-temporal dynamics behind these data requires the next generation of mathematical and statistical algorithms based on quantitative models of human and physical dynamics. This thesis presents the recent developments in this area with methodological advances and various real-world applications. We develop new theoretical and algorithmic techniques for capturing the dynamics of real-world spatio-temporal data by combining cutting-edge machine learning and classical statistical models. We also formulate the sequential decision-making processes as different optimization problems in a data driven manner, suggesting better decisions by taking advantage of the historical knowledge. Last but not least, we investigate a wide array of real-world spatio-temporal datasets using our proposed methods. The results demonstrate the value of spatio-temporal analytics in understanding computational, physical, and social systems.

Date

2022-04-20

Resource Type

Text

Resource Subtype

Dissertation

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