Online discovery of group level events in time series

Xi C. Chen, Abdullah Mueen, Vijay K. Narayanan, Nikos Karampatziakis, Gagan Bansal, Vipin Kumar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations


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 timeseries 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 2 real world datasets and show that the framework detects group level contextual change events that can be explained by plausible causes.

Original languageEnglish (US)
Title of host publicationSIAM International Conference on Data Mining 2014, SDM 2014
EditorsMohammed Zaki, Zoran Obradovic, Pang Ning-Tan, Arindam Banerjee, Chandrika Kamath, Srinivasan Parthasarathy
PublisherSociety for Industrial and Applied Mathematics Publications
Number of pages9
ISBN (Electronic)9781510811515
StatePublished - 2014
Event14th SIAM International Conference on Data Mining, SDM 2014 - Philadelphia, United States
Duration: Apr 24 2014Apr 26 2014

Publication series

NameSIAM International Conference on Data Mining 2014, SDM 2014


Other14th SIAM International Conference on Data Mining, SDM 2014
Country/TerritoryUnited States

Bibliographical note

Funding Information:
Part of this work was done when the first author was an intern in the Cloud & Information Services Lab in Microsoft. It was supported in part by the National Science Foundation under Grants IIS-1029711 and IIS- 0905581, as well as the Planetary Skin Institute. Access to computing facilities was provided by the University of Minnesota Supercomputing Institute.

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