Mining recent temporal patterns for event detection in multivariate time series data

Authors:
Iyad Batal;Dmitriy Fradkin;James Harrison;Fabian Moerchen;Milos Hauskrecht
Affiliations:
University of Pittsburgh, Pittsburgh, PA, USA;Siemens Corporate Research, Princeton, NJ, USA;University of Virginia, Charlottesville, VA, USA;Siemens Corporate Research, Princeton, NJ, USA;University of Pittsburgh, Pittsburgh, PA, USA
Venue:
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Year:
2012

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Abstract

Improving the performance of classifiers using pattern mining techniques has been an active topic of data mining research. In this work we introduce the recent temporal pattern mining framework for finding predictive patterns for monitoring and event detection problems in complex multivariate time series data. This framework first converts time series into time-interval sequences of temporal abstractions. It then constructs more complex temporal patterns backwards in time using temporal operators. We apply our framework to health care data of 13,558 diabetic patients and show its benefits by efficiently finding useful patterns for detecting and diagnosing adverse medical conditions that are associated with diabetes.