THESIS DEFENSE Department Of Computer Science and Engineering University of South Carolina Sourav Das ABSTRACT Time-series analysis is used heavily in modelling and forecasting weather, economics, medical data as well as in various other fields. Change point detection (CPD) means finding abrupt changes in the time-series when the statistical property of a certain part of it starts to differ. CPD has attracted a lot of attention in the artificial intelligence, machine learning and data mining communities. In this thesis, a novel CPD algorithm is introduced for segmenting multivariate time-series data. The proposed algorithm is a general pipeline to process any high dimensional multivariate time-series data using non-linear non-parametric dynamic system. It consists of manifold learning technique for dimensionality reduction, Gaussian process regression to model the non-linear dynamics of the data and predict the next possible time-step, as well as outlier detection based on Mahalanobis distance to determine the change points. The performance of the new CPD algorithm is assessed on synthetic as well as real-world data for validation. The pipeline is used on economic data to predict recession. Finally, functional magnetic resonance imaging (fMRI) data of larval zebrafish is used to segment regions of homogeneous brain activity.