Change Point Detection for Streaming High-Dimensional Time Series

Abstract

An important task in analysing high-dimensional time series data generated from sensors in the Internet of Things (IoT) platform is to detect changes in the statistical properties of the time series. Accurate, efficient and near real-time detection of change points in such data is challenging due to the streaming nature of it and the presence of irrelevant time series dimensions. In this paper, we propose an unsupervised Information Gain and permutation test based change point detection method that does not require a user-defined threshold on change point scores and can accurately identify changes in a sequential setting only using a fixed short memory. Experimental results show that our efficient method improves the accuracy of change point detection compared to two benchmark methods.