Ranking and significance of variable-length similarity-based time series motifs

The detection of very similar patterns in a time series, commonly called motifs, has received continuous and increasing attention from diverse scientific communities. In particular, recent approaches for discovering similar motifs of different lengths have been proposed. In this work, we show that s...

Descripción completa

Detalles Bibliográficos
Autores: Serra, J., Serra, I., Corral, A., Arcos, J.L.
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2016
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:2072/377762
Acceso en línea:http://hdl.handle.net/2072/377762
Access Level:acceso abierto
Palabra clave:Matemàtiques
51
id ES_504e3783c5bbb447bd13c8a9502b3348
oai_identifier_str oai:recercat.cat:2072/377762
network_acronym_str ES
network_name_str España
repository_id_str
spelling Ranking and significance of variable-length similarity-based time series motifsSerra, J.Serra, I.Corral, A.Arcos, J.L.Matemàtiques51The detection of very similar patterns in a time series, commonly called motifs, has received continuous and increasing attention from diverse scientific communities. In particular, recent approaches for discovering similar motifs of different lengths have been proposed. In this work, we show that such variable-length similarity-based motifs cannot be directly compared, and hence ranked, by their normalized dissimilarities. Specifically, we find that length-normalized motif dissimilarities still have intrinsic dependencies on the motif length, and that lowest dissimilarities are particularly affected by this dependency. Moreover, we find that such dependencies are generally non-linear and change with the considered data set and dissimilarity measure. Based on these findings, we propose a solution to rank those motifs and measure their significance. This solution relies on a compact but accurate model of the dissimilarity space, using a beta distribution with three parameters that depend on the motif length in a non-linear way. We believe the incomparability of variable-length dissimilarities could go beyond the field of time series, and that similar modeling strategies as the one used here could be of help in a more broad context.2016info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersion9 p.application/pdfhttp://hdl.handle.net/2072/377762RECERCAT (Dipòsit de la Recerca de Catalunya)reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésExpert Systems with ApplicationsL'accés als continguts d'aquest document queda condicionat a l'acceptació de les condicions d'ús establertes per la següent llicència Creative Commons:http://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:recercat.cat:2072/3777622026-05-29T05:05:01Z
dc.title.none.fl_str_mv Ranking and significance of variable-length similarity-based time series motifs
title Ranking and significance of variable-length similarity-based time series motifs
spellingShingle Ranking and significance of variable-length similarity-based time series motifs
Serra, J.
Matemàtiques
51
title_short Ranking and significance of variable-length similarity-based time series motifs
title_full Ranking and significance of variable-length similarity-based time series motifs
title_fullStr Ranking and significance of variable-length similarity-based time series motifs
title_full_unstemmed Ranking and significance of variable-length similarity-based time series motifs
title_sort Ranking and significance of variable-length similarity-based time series motifs
dc.creator.none.fl_str_mv Serra, J.
Serra, I.
Corral, A.
Arcos, J.L.
author Serra, J.
author_facet Serra, J.
Serra, I.
Corral, A.
Arcos, J.L.
author_role author
author2 Serra, I.
Corral, A.
Arcos, J.L.
author2_role author
author
author
dc.subject.none.fl_str_mv Matemàtiques
51
topic Matemàtiques
51
description The detection of very similar patterns in a time series, commonly called motifs, has received continuous and increasing attention from diverse scientific communities. In particular, recent approaches for discovering similar motifs of different lengths have been proposed. In this work, we show that such variable-length similarity-based motifs cannot be directly compared, and hence ranked, by their normalized dissimilarities. Specifically, we find that length-normalized motif dissimilarities still have intrinsic dependencies on the motif length, and that lowest dissimilarities are particularly affected by this dependency. Moreover, we find that such dependencies are generally non-linear and change with the considered data set and dissimilarity measure. Based on these findings, we propose a solution to rank those motifs and measure their significance. This solution relies on a compact but accurate model of the dissimilarity space, using a beta distribution with three parameters that depend on the motif length in a non-linear way. We believe the incomparability of variable-length dissimilarities could go beyond the field of time series, and that similar modeling strategies as the one used here could be of help in a more broad context.
publishDate 2016
dc.date.none.fl_str_mv 2016
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/2072/377762
url http://hdl.handle.net/2072/377762
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Expert Systems with Applications
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 9 p.
application/pdf
dc.source.none.fl_str_mv RECERCAT (Dipòsit de la Recerca de Catalunya)
reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869407873751056385
score 15,811543

Similares en LA Referencia