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• W2238135223 abstract "Previous chapter Next chapter Full AccessProceedings Proceedings of the 2011 SIAM International Conference on Data Mining (SDM)Time Series Motifs Statistical SignificanceNuno Castro and Paulo J. AzevedoNuno CastroComputer Science and Technology Center, Department of Informatics, University of Minho, PortugalComputer Science and Technology Center, Department of Informatics, University of Minho, Portugal*Nuno Castro is supported by Fundação para a Ciěncia e a Tecnologia grant SFRH/BD/33303/2008.Search for more papers by this author and Paulo J. AzevedoComputer Science and Technology Center, Department of Informatics, University of Minho, PortugalComputer Science and Technology Center, Department of Informatics, University of Minho, Portugal†Paulo J. Azevedo is supported by Fundação para a Ciěncia e Tecnologia, Project ProtUnf, FEDER and Programa de Financiamento Plurianual de Unidades de I&D.Search for more papers by this authorpp.687 - 698Chapter DOI:https://doi.org/10.1137/1.9781611972818.59PDFBibTexSections ToolsAdd to favoritesDownload CitationsTrack CitationsEmail SectionsAboutAbstract Time series motif discovery is the task of extracting previously unknown recurrent patterns from time series data. It is an important problem within applications that range from finance to health. Many algorithms have been proposed for the task of efficiently finding motifs. Surprisingly, most of these proposals do not focus on how to evaluate the discovered motifs. They are typically evaluated by human experts. This is unfeasible even for moderately sized datasets, since the number of discovered motifs tends to be prohibitively large. Statistical significance tests are widely used in bioinformatics and association rules mining communities to evaluate the extracted patterns. In this work we present an approach to calculate time series motifs statistical significance. Our proposal leverages work from the bioinformatics community by using a symbolic definition of time series motifs to derive each motif's p-value. We estimate the expected frequency of a motif by using Markov Chain models. The p-value is then assessed by comparing the actual frequency to the estimated one using statistical hypothesis tests. Our contribution gives means to the application of a powerful technique – statistical tests – to a time series setting. This provides researchers and practitioners with an important tool to evaluate automatically the degree of relevance of each extracted motif. Previous chapter Next chapter RelatedDetails Published:2011ISBN:978-0-89871-992-5eISBN:978-1-61197-281-8 https://doi.org/10.1137/1.9781611972818Book Series Name:ProceedingsBook Code:PRDT11Book Pages:1-1015Key words:Time Series, Motif Discovery, Statistical Significance tests, Significant Patterns" @default.
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• W2238135223 title "Time Series Motifs Statistical Significance" @default.
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