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Performing event detection in time series with SwiftEvent: an algorithm with supervised learning of detection criteria

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Abstract

The automated detection of points in a time series with a special meaning to a user, commonly referred to as the detection of events, is an important aspect of temporal data mining. These events often are points in a time series that can be peaks, level changes, sudden changes of spectral characteristics, etc. Fast algorithms are needed for event detection for online applications or applications with huge time series data sets. In this article, we present a very fast algorithm for event detection that learns detection criteria from labeled sample time series (i.e., time series where events are marked). This algorithm is based on fast transformations of time series into low-dimensional feature spaces and probabilistic modeling techniques to identify criteria in a supervised manner. Events are then found in one, single fast pass over the signal (therefore, the algorithm is called SwiftEvent) by evaluating learned thresholds on Mahalanobis distances in the feature space. We analyze the run-time complexity of SwiftEvent and demonstrate its application in some use cases with artificial and real-world data sets in comparison with other state-of-the-art techniques.

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Acknowledgements

This project (HA Project No. 472/15-14) is funded in the framework of Hessen ModellProjekte, financed with funds of LOEWE Landes-Offensive zur Entwicklung Wissenschaftlich-ökonomischer Exzellenz, Förderlinie 3: KMU-Verbundvorhaben (State Offensive for the Development of Scientific and Economic Excellence).

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  1. Intelligent Embedded Systems Group, University of Kassel, Wilhelmshöher Allee 71 - 73, 34121, Kassel, Germany

    André Gensler & Bernhard Sick

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Correspondence to Bernhard Sick.

Appendix

Appendix

The parameters for the case studies (see Sect. 7) are shown in detail in Tables 8, 9, and 10. They correspond to the results from Tables 1,2, 3, and 4.

Table 8 Experiment parameters for the waveform set
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Table 9 Experiment parameters for the ECG case study
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Table 10 Experiment parameters for the building case study
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Gensler, A., Sick, B. Performing event detection in time series with SwiftEvent: an algorithm with supervised learning of detection criteria. Pattern Anal Applic 21, 543–562 (2018). https://doi.org/10.1007/s10044-017-0657-0

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