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dc.contributor.authorBaldán Lozano, Francisco Javier 
dc.contributor.authorBenítez Sánchez, José Manuel 
dc.date.accessioned2022-04-22T12:05:45Z
dc.date.available2022-04-22T12:05:45Z
dc.date.issued2021-11-02
dc.identifier.citationBaldán, F.J... [et al.]. SCMFTS: Scalable and Distributed Complexity Measures and Features for Univariate and Multivariate Time Series in Big Data Environments. Int J Comput Intell Syst 14, 186 (2021). [https://doi.org/10.1007/s44196-021-00036-7]es_ES
dc.identifier.urihttp://hdl.handle.net/10481/74479
dc.descriptionThis research has been partially funded by the following grants: TIN2016-81113-R from the Spanish Ministry of Economy and Competitiveness, P12-TIC-2985 and P18-TP-5168 from Andalusian Regional Government, Spain, and EU Commission with FEDER funds. Francisco J. Baldan holds the FPI grant BES-2017-080137 from the Spanish Ministry of Economy and Competitiveness. D. Peralta is a Postdoctoral Fellow of the Research Foundation of Flanders (170303/12X1619N). Y. Saeys is an ISAC Marylou Ingram Scholar.es_ES
dc.description.abstractTime series data are becoming increasingly important due to the interconnectedness of the world. Classical problems, which are getting bigger and bigger, require more and more resources for their processing, and Big Data technologies offer many solutions. Although the principal algorithms for traditional vector-based problems are available in Big Data environments, the lack of tools for time series processing in these environments needs to be addressed. In this work, we propose a scalable and distributed time series transformation for Big Data environments based on well-known time series features (SCMFTS), which allows practitioners to apply traditional vector-based algorithms to time series problems. The proposed transformation, along with the algorithms available in Spark, improved the best results in the state-of-the-art on the Wearable Stress and Affect Detection dataset, which is the biggest publicly available multivariate time series dataset in the University of California Irvine (UCI) Machine Learning Repository. In addition, SCMFTS showed a linear relationship between its runtime and the number of processed time series, demonstrating a linear scalable behavior, which is mandatory in Big Data environments. SCMFTS has been implemented in the Scala programming language for the Apache Spark framework, and the code is publicly available.es_ES
dc.description.sponsorshipSpanish Government TIN2016-81113-R BES-2017-080137es_ES
dc.description.sponsorshipAndalusian Regional Government, Spain P12-TIC-2985 P18-TP-5168es_ES
dc.description.sponsorshipEuropean Commission European Commission Joint Research Centre European Commissiones_ES
dc.language.isoenges_ES
dc.publisherSpringeres_ES
dc.rightsAtribución 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectTime serieses_ES
dc.subjectTime series featureses_ES
dc.subjectFeature-based approaches_ES
dc.subjectBig Dataes_ES
dc.subjectScalabilityes_ES
dc.titleSCMFTS: Scalable and Distributed Complexity Measures and Features for Univariate and Multivariate Time Series in Big Data Environmentses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.identifier.doi10.1007/s44196-021-00036-7
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones_ES


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Atribución 3.0 España
Except where otherwise noted, this item's license is described as Atribución 3.0 España