TA4L: Efficient temporal abstraction of multivariate time series
dc.contributor.author
dc.date.accessioned
2023-03-24T10:03:45Z
dc.date.available
2023-03-24T10:03:45Z
dc.date.issued
2022-05-23
dc.identifier.issn
0950-7051
dc.identifier.uri
dc.description.abstract
In this work, we introduce TA4L, a new efficient algorithm to transform multivariate time series into Lexicographical Symbolic Time Interval Sequences (LSTISs), that is, sequences ready to feed time-interval related pattern (TIRP) mining algorithms. The ultimate goal is to make explicit the embedded, ad-hoc pre-processes related to TIRP mining algorithms while offering an efficient solution for the required pre-processing. On the one hand, TA4L divides the signals into segments based on time duration (instead of the often-used practice based on the number of samples), which allows the construction of consistent time intervals. Concatenation of intervals is controlled by a maximum time gap constraint that reinforces the generated time intervals’ consistency. Moreover, different ways to parallelise the algorithm are explored that are accompanied by efficient data structures to speed up the pre-processing cost. TA4L has been experimentally evaluated with synthetic and real datasets, and the results show that TA4L requires significantly less computation time than other state-of-the-art approaches, revealing that it is an effective algorithm
dc.description.sponsorship
This project received joint funding from ERDF, the Spanish Ministry of the Economy, Industry and Competitiveness (MINECO) and the National Agency for Research , under grant no. RTC 2017-6071-1 (SERAS). The work was carried out with support from the Generalitat de Catalunya 2017 SGR 1551, a predoctoral grant from the University of Girona (grants for researchers in training/IFUdG2017) and a mobility grant (additional support for the mobility of UdG researchers/MOB2019).
Open Access funding provided thanks to the CRUE-CSIC agreement with Elsevier
dc.format.mimetype
application/pdf
dc.language.iso
eng
dc.publisher
Elsevier
dc.relation.isformatof
Reproducció digital del document publicat a: https://doi.org/10.1016/j.knosys.2022.108554
dc.relation.ispartof
Knowledge-Based Systems, 2022, vol. 244, art.núm. 108554
dc.relation.ispartofseries
Articles publicats (D-EEEiA)
dc.rights
Attribution 4.0 International
dc.rights.uri
dc.title
TA4L: Efficient temporal abstraction of multivariate time series
dc.type
info:eu-repo/semantics/article
dc.rights.accessRights
info:eu-repo/semantics/openAccess
dc.type.version
info:eu-repo/semantics/publishedVersion
dc.identifier.doi
dc.identifier.idgrec
035190
dc.type.peerreviewed
peer-reviewed
dc.identifier.eissn
1872-7409