Abstract
Time series are sequences of observations ordered by time. Detecting outliers in a set of time series is very important for many use cases, including fraud detection and predictive maintenance. However, this task continues to be difficult: First, time series may be of different lengths and conventional distance measures like the Euclidean distance can not capture their similarity well. Workarounds like feature engineering require domain knowledge and render solutions domain-specific. Second, many existing techniques are supervised, but training labels are expensive if not impossible to obtain. In this paper, we propose Multi-Kernel Times Series Outlier Detection (MK-TSOD), a method that combines the Fourier Transform, Global Alignment Kernels, and Multiple Kernel Learning with Support Vector Data Description. We describe its specifics, and show that MK-TSOD outperforms existing methods on standard benchmark data.
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Notes
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We abbreviate the data sets “ChlorineConcentration”, “ToeSegmentation1”, and “ToeSegmentation2” as “Ch.Concent.”, “ToeSeg1”, and “ToeSeg2”, respectively.
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Here, MK denotes MK-TSOD; DTW denotes DTW-SVDD.
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Acknowledgements
This work was supported by the DFG Research Training Group 2153: “Energy Status Data — Informatics Methods for its Collection, Analysis and Exploitation”.
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Kalinke, F., Fouché, E., Thiessen, H., Böhm, K. (2023). Multi-kernel Times Series Outlier Detection. In: Bifet, A., Lorena, A.C., Ribeiro, R.P., Gama, J., Abreu, P.H. (eds) Discovery Science. DS 2023. Lecture Notes in Computer Science(), vol 14276. Springer, Cham. https://doi.org/10.1007/978-3-031-45275-8_46
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