Unsupervised methods to discover events from spatio-temporal data

Abstract

Unsupervised event detection in spatio-temporal data aims to autonomously identify when and/or where events occurred with little or no human supervision. It is an active field of research with notable applications in social, Earth, and medical sciences. While event detection has enjoyed tremendous success in many domains, it is still a challenging problem due to the vastness of data points, presence of noise and missing values, the heterogeneous nature of spatio-temporal signals, and the large variety of event types. Unsupervised event detection is a broad and yet open research area. Instead of exploring every aspect in this area, this dissertation focuses on four novel algorithms that covers two types of important events in spatio-temporal data: change-points and moving regions. The first algorithm in this dissertation is the Persistence-Consistency (PC) framework. It is a general framework that can increase the robustness of change-point detection algorithms to noise and outliers. The major advantage of the PC framework is that it can work with most modeling-based change-point detection algorithms and improve their performance without modifying the selected change-point detection algorithm. We use two real-world applications, forest fire detection using a satellite dataset and activity segmentation from a mobile health dataset, to test the effectiveness of this framework. The second and third algorithms in this dissertation are proposed to detect a novel type of change point, which is named as contextual change points. While most existing change points more or less indicate that the time series is different from what it was before, a contextual change point typically suggests an event that causes the relationship of several time series changes. Each of these two algorithms introduces one type of contextual change point and also presents an algorithm to detect the corresponding type of change point. We demonstrate the unique capabilities of these approaches with two applications: event detection in stock market data and forest fire detection using remote sensing data. The final algorithm in this dissertation is a clustering method that discovers a particular type of moving regions (or dynamic spatio-temporal patterns) in noisy, incomplete, and heterogeneous data. This task faces two major challenges: First, the regions (or clusters) are dynamic and may change in size, shape, and statistical properties over time. Second, numerous spatio-temporal data are incomplete, noisy, heterogeneous, and highly variable (over space and time). Our proposed approach fully utilizes the spatial contiguity and temporal similarity in the spatio-temporal data and, hence, can address the above two challenges. We demonstrate the performance of the proposed method on a real-world application of monitoring in-land water bodies on a global scale.