Change Point Detection in High-Dimensional Streaming Time Series Data

© 2019 Masoomeh ZameniIn the Internet of Things (IoT), data is continuously recorded from different data sources, such as sensors for monitoring purposes. Some examples of these IoT based sensing platforms are loop detector sensors employed in different road segments of cities, various sensors used in smart home services, and sensors attached on a patient's body in hospital health care monitoring systems. The recorded data usually generates an unbounded sequence of data points, called a streaming time series, in which a new sequence of data points arrives at each time stamp and each data point has a time stamp. In multi-sensor environments, usually each sensor produces a streaming time series, which leads to a high-dimensional time series, where each time series dimension is the data recorded from a sensor. Time series change point detection aims at finding the time points when the behavior of a monitored system changes significantly. These time points are called change points. A change point usually leads to a significant change in the statistical properties of the time series, such as the mean, variance, or probability distribution of the data points before and after the change point. Change point detection is applied in many real-life applications. For example, real-time traffic congestion detection is an example where change point detection can be used to improve city management, and to provide timely advice to drivers. In this thesis we propose accurate and efficient change point detection approaches for streaming high-dimensional time series and a framework for detection of unexpected changes in high-dimensional streaming time series. We show that the proposed methods achieve high accuracy and are efficient in detecting change points in high-dimensional streaming time series