{"id":"https://openalex.org/W2030863907","doi":"https://doi.org/10.1007/s10618-010-0179-5","title":"Time series shapelets: a novel technique that allows accurate, interpretable and fast classification","display_name":"Time series shapelets: a novel technique that allows accurate, interpretable and fast classification","publication_year":2010,"publication_date":"2010-06-17","ids":{"openalex":"https://openalex.org/W2030863907","doi":"https://doi.org/10.1007/s10618-010-0179-5","mag":"2030863907"},"language":"en","primary_location":{"id":"doi:10.1007/s10618-010-0179-5","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10618-010-0179-5","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10618-010-0179-5.pdf","source":{"id":"https://openalex.org/S121920818","display_name":"Data Mining and Knowledge Discovery","issn_l":"1384-5810","issn":["1384-5810","1573-756X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Data Mining and Knowledge Discovery","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/s10618-010-0179-5.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5055991377","display_name":"Lexiang Ye","orcid":null},"institutions":[{"id":"https://openalex.org/I103635307","display_name":"University of California, Riverside","ror":"https://ror.org/03nawhv43","country_code":"US","type":"education","lineage":["https://openalex.org/I103635307"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Lexiang Ye","raw_affiliation_strings":["Department of Computer Science and Engineering, University of California, Riverside, CA, 92521, USA","Department of Computer Science and Engineering, University of California, Riverside, USA 92521#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, University of California, Riverside, CA, 92521, USA","institution_ids":["https://openalex.org/I103635307"]},{"raw_affiliation_string":"Department of Computer Science and Engineering, University of California, Riverside, USA 92521#TAB#","institution_ids":["https://openalex.org/I103635307"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5078245746","display_name":"Eamonn Keogh","orcid":"https://orcid.org/0000-0002-4188-3968"},"institutions":[{"id":"https://openalex.org/I103635307","display_name":"University of California, Riverside","ror":"https://ror.org/03nawhv43","country_code":"US","type":"education","lineage":["https://openalex.org/I103635307"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Eamonn Keogh","raw_affiliation_strings":["Department of Computer Science and Engineering, University of California, Riverside, CA, 92521, USA","Department of Computer Science and Engineering, University of California, Riverside, USA 92521#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, University of California, Riverside, CA, 92521, USA","institution_ids":["https://openalex.org/I103635307"]},{"raw_affiliation_string":"Department of Computer Science and Engineering, University of California, Riverside, USA 92521#TAB#","institution_ids":["https://openalex.org/I103635307"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5055991377"],"corresponding_institution_ids":["https://openalex.org/I103635307"],"apc_list":{"value":2390,"currency":"EUR","value_usd":2990},"apc_paid":{"value":2390,"currency":"EUR","value_usd":2990},"fwci":6.4336,"has_fulltext":true,"cited_by_count":387,"citation_normalized_percentile":{"value":0.97135781,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":100},"biblio":{"volume":"22","issue":"1-2","first_page":"149","last_page":"182"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11270","display_name":"Complex Systems and Time Series Analysis","score":0.9890999794006348,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9735000133514404,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.693273663520813},{"id":"https://openalex.org/keywords/k-nearest-neighbors-algorithm","display_name":"k-nearest neighbors algorithm","score":0.6820484399795532},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.640743613243103},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4911668002605438},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4760451316833496},{"id":"https://openalex.org/keywords/nearest-neighbor-search","display_name":"Nearest neighbor search","score":0.4645725190639496},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.4642779231071472},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4304468333721161},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.42192861437797546},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3961416184902191},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.38613325357437134}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.693273663520813},{"id":"https://openalex.org/C113238511","wikidata":"https://www.wikidata.org/wiki/Q1071612","display_name":"k-nearest neighbors algorithm","level":2,"score":0.6820484399795532},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.640743613243103},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4911668002605438},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4760451316833496},{"id":"https://openalex.org/C116738811","wikidata":"https://www.wikidata.org/wiki/Q608751","display_name":"Nearest neighbor search","level":2,"score":0.4645725190639496},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.4642779231071472},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4304468333721161},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.42192861437797546},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3961416184902191},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.38613325357437134},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1007/s10618-010-0179-5","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10618-010-0179-5","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10618-010-0179-5.pdf","source":{"id":"https://openalex.org/S121920818","display_name":"Data Mining and Knowledge Discovery","issn_l":"1384-5810","issn":["1384-5810","1573-756X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Data Mining and Knowledge Discovery","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1007/s10618-010-0179-5","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10618-010-0179-5","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10618-010-0179-5.pdf","source":{"id":"https://openalex.org/S121920818","display_name":"Data Mining and Knowledge Discovery","issn_l":"1384-5810","issn":["1384-5810","1573-756X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Data Mining and Knowledge Discovery","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/8","score":0.5199999809265137,"display_name":"Decent work and economic growth"}],"awards":[{"id":"https://openalex.org/G2101647459","display_name":"Tools to Mine and Index Trajectories of Physical Artifacts","funder_award_id":"0803410","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G8377684302","display_name":"III-CXT-Large: Collaborative Research: Interactive and intelligent searching of biological images by query and network navigation with learning capabilities","funder_award_id":"0808770","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2030863907.pdf","grobid_xml":"https://content.openalex.org/works/W2030863907.grobid-xml"},"referenced_works_count":28,"referenced_works":["https://openalex.org/W116902681","https://openalex.org/W1549672291","https://openalex.org/W1594031697","https://openalex.org/W1785021972","https://openalex.org/W1840899162","https://openalex.org/W1853995153","https://openalex.org/W1973366456","https://openalex.org/W1994383689","https://openalex.org/W2006761268","https://openalex.org/W2006783944","https://openalex.org/W2029438113","https://openalex.org/W2039260438","https://openalex.org/W2091921805","https://openalex.org/W2098759488","https://openalex.org/W2112791883","https://openalex.org/W2112865076","https://openalex.org/W2118938540","https://openalex.org/W2128061541","https://openalex.org/W2129308322","https://openalex.org/W2134262590","https://openalex.org/W2134421465","https://openalex.org/W2159594638","https://openalex.org/W2164274563","https://openalex.org/W3085162807","https://openalex.org/W3147178137","https://openalex.org/W4252684946","https://openalex.org/W4285719527","https://openalex.org/W6608681859"],"related_works":["https://openalex.org/W2148008870","https://openalex.org/W2381195555","https://openalex.org/W4246757943","https://openalex.org/W2368606575","https://openalex.org/W2132753198","https://openalex.org/W2369874856","https://openalex.org/W2182477562","https://openalex.org/W2792185758","https://openalex.org/W2787484455","https://openalex.org/W2119808169"],"abstract_inverted_index":{"Classification":[0],"of":[1,15,56,67,123,159],"time":[2,39,85,136,139,149,197],"series":[3,40,137,140,150,198],"has":[4,23],"been":[5,18],"attracting":[6],"great":[7],"interest":[8],"over":[9],"the":[10,27,54,58,70,78,110,115,124,165,168,172,175,196],"past":[11],"decade.":[12],"While":[13,46],"dozens":[14],"techniques":[16],"have":[17],"introduced,":[19],"recent":[20],"empirical":[21,187],"evidence":[22],"strongly":[24],"suggested":[25],"that":[26,89],"simple":[28],"nearest":[29,59,71,125,176],"neighbor":[30,60,72,126,177],"algorithm":[31,73],"is":[32],"very":[33],"difficult":[34],"to":[35,105,113,167,174,178],"beat":[36],"for":[37,43],"most":[38],"problems,":[41],"especially":[42,93],"large-scale":[44],"datasets.":[45],"this":[47,130],"may":[48],"be":[49,202],"considered":[50],"good":[51],"news,":[52],"given":[53],"simplicity":[55],"implementing":[57],"algorithm,":[61],"there":[62],"are":[63,148,153],"some":[64,107,155],"negative":[65],"consequences":[66],"this.":[68],"First,":[69],"requires":[74],"storing":[75],"and":[76,86,112,206],"searching":[77],"entire":[79],"dataset,":[80],"resulting":[81],"in":[82,154,189],"a":[83,134,160],"high":[84],"space":[87],"complexity":[88],"limits":[90],"its":[91],"applicability,":[92],"on":[94,195],"resource-limited":[95],"sensors.":[96],"Second,":[97],"beyond":[98],"mere":[99],"classification":[100,116,192],"accuracy,":[101],"we":[102,132,182],"often":[103],"wish":[104],"gain":[106],"insight":[108],"into":[109],"data":[111],"make":[114],"result":[117],"more":[118,204],"explainable,":[119],"which":[120,142,152],"global":[121],"characteristics":[122],"cannot":[127],"provide.":[128],"In":[129],"work":[131],"introduce":[133],"new":[135],"primitive,":[138],"shapelets,":[141],"addresses":[143],"these":[144],"limitations.":[145],"Informally,":[146],"shapelets":[147],"subsequences":[151],"sense":[156],"maximally":[157],"representative":[158],"class.":[161],"We":[162],"can":[163,201],"use":[164],"distance":[166,173],"shapelet,":[169],"rather":[170],"than":[171,209],"classify":[179],"objects.":[180],"As":[181],"shall":[183],"show":[184],"with":[185],"extensive":[186],"evaluations":[188],"diverse":[190],"domains,":[191],"algorithms":[193],"based":[194],"shapelet":[199],"primitives":[200],"interpretable,":[203],"accurate,":[205],"significantly":[207],"faster":[208],"state-of-the-art":[210],"classifiers.":[211]},"counts_by_year":[{"year":2026,"cited_by_count":8},{"year":2025,"cited_by_count":28},{"year":2024,"cited_by_count":40},{"year":2023,"cited_by_count":51},{"year":2022,"cited_by_count":44},{"year":2021,"cited_by_count":43},{"year":2020,"cited_by_count":31},{"year":2019,"cited_by_count":26},{"year":2018,"cited_by_count":28},{"year":2017,"cited_by_count":17},{"year":2016,"cited_by_count":19},{"year":2015,"cited_by_count":15},{"year":2014,"cited_by_count":18},{"year":2013,"cited_by_count":12},{"year":2012,"cited_by_count":6}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}