thesis

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Abstract

Department of Computer Science and EngineeringTime series data is everywhere, such as stock data in finance market, the sensor data in factories, or the temperature data in everyday life. Time series data have been studied for a long time to analyze and predict the future behavior. While it is tractable when the sequential data behave as stationary, it becomes difficult to model and predict non-stationary time series. Change point detection is a problem that identify non-stationarity which has been investigated for decades in many different names. Change point detection is a challenging problem because defining a change point decisively and objectively is difficult in nature. In this thesis we are trying to define and find a change point using hypothesis tests based on statistics. Specifically we focus on structural breaks in the covariance structure of Gaussian Processes. Further we propose an online change point detection algorithm, called Confirmatory Bayesian Online Change Point Detection, by leveraging the devised hypothesis tests into the conventional Bayesian online change point detection algorithm.clos

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