Estimation and Identification of Spatio-Temporal Models with Applications in Engineering, Healthcare and Social Science

Several natural phenomena are known to exhibit a spatio-temporal evolution process. The study of such processes, which is pivotal to our understanding of how best to predict and control spatio-temporal systems, has motivated researchers to develop appropriate tools that infer models and their parameters from observed data. This paper reviews this active area of research by providing an insight into the fundamental ideas spanning the development of spatio-temporal models, dimensionality reduction methods and techniques for state and parameter estimation. Recent advances are discussed in the context of novel spatio-temporal approaches proposed for applications in three specific domains - engineering, healthcare and social science. They illustrate the wide applicability of estimation and identification of spatio-temporal processes as novel advances in sensor systems and data collection are used to observe them.