Modeling Multiple Time Series for Anomaly Detection

Authors:
Philip K. Chan;Matthew V. Mahoney
Affiliations:
Florida Institute of Technology;Florida Institute of Technology
Venue:
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Year:
2005

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Abstract

Our goal is to generate comprehensible and accurate models from multiple time series for anomaly detection. The models need to produce anomaly scores in an online manner for real-life monitoring tasks. We introduce three algorithms that work in a constructed feature space and evaluate them with a real data set from the NASA shuttle program. Our offline and online evaluations indicate that our algorithms can be more accurate than two existing algorithms.