Abstract
The present-day accessibility of technology enables easy logging of both sensor values and event logs over extended periods. In this context, detecting abnormal segments in time series data has become an important data mining task. Existing work on anomaly detection focuses either on continuous time series or discrete event logs and not on the combination. However, in many practical applications, the patterns extracted from the event log can reveal contextual and operational conditions of a device that must be taken into account when predicting anomalies in the continuous time series. This paper proposes an anomaly detection method that can handle mixed-type time series. The method leverages frequent pattern mining techniques to construct an embedding of mixed-type time series on which an isolation forest is trained. Experiments on several real-world univariate and multivariate time series, as well as a synthetic mixed-type time series, show that our anomaly detection algorithm outperforms state-of-the-art anomaly detection techniques such as MatrixProfile, Pav, Mifpod and Fpof.
L. Feremans and V. Vercruyssen—These authors contributed equally to the work.
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Notes
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See Table 4 in Appendix A.2 for details on setting preprocessing parameters.
- 3.
Implementation of Pbad: https://bitbucket.org/len_feremans/pbad/.
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The authors would like to thank the VLAIO SBO HYMOP project for funding this research.
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Feremans, L., Vercruyssen, V., Cule, B., Meert, W., Goethals, B. (2020). Pattern-Based Anomaly Detection in Mixed-Type Time Series. In: Brefeld, U., Fromont, E., Hotho, A., Knobbe, A., Maathuis, M., Robardet, C. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2019. Lecture Notes in Computer Science(), vol 11906. Springer, Cham. https://doi.org/10.1007/978-3-030-46150-8_15
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