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Article type: Research Article
Authors: Noering, Fabian Kai-Dietricha; * | Jonas, Konstantinb; 1 | Klawonn, Frankc; d; 1
Affiliations: [a] Volkswagen AG, Wolfsburg, Germany | [b] Volkswagen AG, Deutsche Bahn AG, Germany | [c] Department of Computer Science, Ostfalia University of Applied Sciences, Wolfenbüttel, Germany | [d] Helmholtz Center for Infection Research, Braunschweig, Germany
Correspondence: [*] Corresponding author: Fabian Kai-Dietrich Noering, Volkswagen AG, Wolfsburg, Germany. E-mail: fabian.kai-dietrich.noering@volkswagen.de.
Note: [1] Frank Klawonn and Konstantin Jonas contributed equally to this work.
Abstract: In technical systems the analysis of similar load situations is a promising technique to gain information about the system’s state, its health or wearing. Very often, load situations are challenging to be defined by hand. Hence, these situations need to be discovered as recurrent patterns within multivariate time series data of the system under consideration. Unsupervised algorithms for finding such recurrent patterns in multivariate time series must be able to cope with very large data sets because the system might be observed over a very long time. In our previous work we identified discretization-based approaches to be very interesting for variable length pattern discovery because of their low computing time due to the simplification (symbolization) of the time series. In this paper we propose additional preprocessing steps for symbolic representation of time series aiming for enhanced multivariate pattern discovery. Beyond that we show the performance (quality and computing time) of our algorithms in a synthetic test data set as well as in a real life example with 100 millions of time points. We also test our approach with increasing dimensionality of the time series.
Keywords: Time series data mining, pattern discovery, motif discovery, variable pattern length, unsupervised, multivariate
DOI: 10.3233/IDA-205329
Journal: Intelligent Data Analysis, vol. 25, no. 5, pp. 1051-1072, 2021
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