Online Discovery of Group Level Events in Time Series

Abstract

Recent advances in high throughput data collection and storage technologies have led to a dramatic increase in the availability of high-resolution time series data sets in various domains. These time series reflect the dynamics of the underlying physical processes in these domains. Detecting changes in a time series over time or changes in the relationships among the time series in a data set containing multiple contemporaneous time series can be useful to detect changes in these physical processes. Contextual events detection algorithms detect changes in the relationships between multiple related time series. In this work, we introduce a new type of contextual events, called group level contextual change events. In contrast to individual contextual change events that reflect the change in behavior of one target time series against a context, group level events reflect the change in behavior of a target group of time series relative to a context group of time series. We propose an online framework to detect two types of group level contextual change events: (i) group formation (i.e., detecting when a set of multiple unrelated time series or groups of time series with little prior relationship in their behavior forms a new group of related time series) and (ii) group disbanding (i.e., detecting when one stable set of related time series disbands into two or more subgroups with little relationship in their behavior). We demonstrate this framework using two real world datasets and show that the framework detects group level contextual change events that can be explained by plausible causes.