Online Machine Learning for Inference from Multivariate Time-series
Cyber-physical systems generate multivariate time series that capture the behaviour of physical systems in response to cybernetic inputs.
Rohan Thekkemarickal Money
PhD Candidate
Inference and data analysis over networks have become significant areas of research due to the increasing prevalence of interconnected systems and the growing volume of data they produce. Many of these systems generate data in the form of multi-variate time series, which are collections of time series data that are observed simul- taneously across multiple variables. For example, EEG measurements of the brain produce multivariate time series data that record the electrical activity of different brain regions over time.
Cyber-physical systems generate multivariate time series that capture the behaviour of physical systems in response to cybernetic inputs. Similarly, financial time series reflect the dynamics of multiple financial instruments or market indices over time. Through the analysis of these time series, one can uncover important details about the behavior of the system, detect patterns, and make predictions. Therefore, designing effective methods for data analysis and inference over networks of multi- variate time series is a crucial area of research with numerous applications across various fields.
In this Ph.D. Thesis, our focus is on identifying the directed relation- ships between time series and leveraging this information to design algorithms for data prediction as well as missing data imputation.