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SAXRegEx: Multivariate time series pattern search with symbolic representation, regular expression, and query expansion.

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Title: SAXRegEx: Multivariate time series pattern search with symbolic representation, regular expression, and query expansion.
Authors: Yu, Yuncong1,2 (AUTHOR) yuncong.yu@iav.de, Becker, Tim1 (AUTHOR) tim.becker@iav.de, Trinh, Le Minh1 (AUTHOR) le.minh.trinh@iav.de, Behrisch, Michael2 (AUTHOR) m.behrisch@uu.nl
Source: Computers & Graphics. May2023, Vol. 112, p13-21. 9p.
Subjects: Time pressure, User interfaces, Search algorithms, Time measurements, Automobile industry, Time series analysis
Abstract: We present SAXRegEx, a method for pattern search in multivariate time series in the presence of various distortions, such as duration variation, warping, and time delay between signals. For example, in the automotive industry, calibration engineers spontaneously search for event-induced patterns in new measurements under time pressure. Current methods do not sufficiently address duration (horizontal along the time axis) scaling and inter-track time delay. One reason is that it can be overwhelmingly complex to consider scaling and warping jointly and analyze temporal dynamics and attribute interrelation simultaneously. SAXRegEx meets this challenge with a novel symbolic representation adapted to handle time series with multiple tracks. We employ methods from text retrieval, i.e., regular expression matching, to perform a pattern retrieval and develop a novel query expansion algorithm to deal flexibly with pattern distortions. Experiments show the effectiveness of our approach, especially in the presence of such distortions, and its efficiency surpassing the benchmarking methods. We have developed a user interface with the emphasis on multivariate query definition with inter-track time shifts and algorithm parameter setting. While we design the method primarily for automotive data, it is well transferable to other domains. [Display omitted] • Algorithm tackles the insufficiently addressed duration scaling problem. • Algorithm handles the untouched inter-track time shifts problem. • One of the fastest algorithms for a quick search with reduced accuracy. • First visual query system for multivariate query with inter-track time shifts. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:We present SAXRegEx, a method for pattern search in multivariate time series in the presence of various distortions, such as duration variation, warping, and time delay between signals. For example, in the automotive industry, calibration engineers spontaneously search for event-induced patterns in new measurements under time pressure. Current methods do not sufficiently address duration (horizontal along the time axis) scaling and inter-track time delay. One reason is that it can be overwhelmingly complex to consider scaling and warping jointly and analyze temporal dynamics and attribute interrelation simultaneously. SAXRegEx meets this challenge with a novel symbolic representation adapted to handle time series with multiple tracks. We employ methods from text retrieval, i.e., regular expression matching, to perform a pattern retrieval and develop a novel query expansion algorithm to deal flexibly with pattern distortions. Experiments show the effectiveness of our approach, especially in the presence of such distortions, and its efficiency surpassing the benchmarking methods. We have developed a user interface with the emphasis on multivariate query definition with inter-track time shifts and algorithm parameter setting. While we design the method primarily for automotive data, it is well transferable to other domains. [Display omitted] • Algorithm tackles the insufficiently addressed duration scaling problem. • Algorithm handles the untouched inter-track time shifts problem. • One of the fastest algorithms for a quick search with reduced accuracy. • First visual query system for multivariate query with inter-track time shifts. [ABSTRACT FROM AUTHOR]
ISSN:00978493
DOI:10.1016/j.cag.2023.03.002