Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/55607
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dc.contributor.authorIldar Batyrshinen_US
dc.contributor.authorThongchai Dumrongpokaphanen_US
dc.contributor.authorVladik Kreinovichen_US
dc.contributor.authorOlga Koshelevaen_US
dc.date.accessioned2018-09-05T02:58:24Z-
dc.date.available2018-09-05T02:58:24Z-
dc.date.issued2016-01-01en_US
dc.identifier.issn16113349en_US
dc.identifier.issn03029743en_US
dc.identifier.other2-s2.0-85006021701en_US
dc.identifier.other10.1007/978-3-319-49046-5_39en_US
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85006021701&origin=inwarden_US
dc.identifier.urihttp://cmuir.cmu.ac.th/jspui/handle/6653943832/55607-
dc.description.abstract© Springer International Publishing AG 2016. When practitioners analyze the similarity between time series, they often use correlation to gauge this similarity. Sometimes this works, but sometimes, this leads to counter-intuitive results, in which case other similarity measures are more appropriate. An important question is how to select an appropriate similarity measures. In this paper, we show, on simple examples, that the use of natural symmetries – scaling and shift – can help with such a selection.en_US
dc.subjectComputer Scienceen_US
dc.subjectMathematicsen_US
dc.titleHow to select an appropriate similarity measure: Towards a symmetry-based approachen_US
dc.typeBook Seriesen_US
article.title.sourcetitleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)en_US
article.volume9978 LNAIen_US
article.stream.affiliationsInstituto Politecnico Nacionalen_US
article.stream.affiliationsChiang Mai Universityen_US
article.stream.affiliationsUniversity of Texas at El Pasoen_US
Appears in Collections:CMUL: Journal Articles

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