{"id":"https://openalex.org/W3036081019","doi":"https://doi.org/10.1007/s10618-020-00690-z","title":"TEASER: early and accurate time series classification","display_name":"TEASER: early and accurate time series classification","publication_year":2020,"publication_date":"2020-06-16","ids":{"openalex":"https://openalex.org/W3036081019","doi":"https://doi.org/10.1007/s10618-020-00690-z","mag":"3036081019"},"language":"en","primary_location":{"id":"doi:10.1007/s10618-020-00690-z","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10618-020-00690-z","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10618-020-00690-z.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","license_id":"https://openalex.org/licenses/cc-by","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/s10618-020-00690-z.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5103083154","display_name":"Patrick Sch\u00e4fer","orcid":"https://orcid.org/0000-0003-2244-6065"},"institutions":[{"id":"https://openalex.org/I39343248","display_name":"Humboldt-Universit\u00e4t zu Berlin","ror":"https://ror.org/01hcx6992","country_code":"DE","type":"education","lineage":["https://openalex.org/I39343248"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Patrick Sch\u00e4fer","raw_affiliation_strings":["Humboldt University of Berlin, Berlin, Germany"],"raw_orcid":"https://orcid.org/0000-0003-2244-6065","affiliations":[{"raw_affiliation_string":"Humboldt University of Berlin, Berlin, Germany","institution_ids":["https://openalex.org/I39343248"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5055236937","display_name":"Ulf Leser","orcid":"https://orcid.org/0000-0003-2166-9582"},"institutions":[{"id":"https://openalex.org/I39343248","display_name":"Humboldt-Universit\u00e4t zu Berlin","ror":"https://ror.org/01hcx6992","country_code":"DE","type":"education","lineage":["https://openalex.org/I39343248"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Ulf Leser","raw_affiliation_strings":["Humboldt University of Berlin, Berlin, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Humboldt University of Berlin, Berlin, Germany","institution_ids":["https://openalex.org/I39343248"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5103083154"],"corresponding_institution_ids":["https://openalex.org/I39343248"],"apc_list":{"value":2390,"currency":"EUR","value_usd":2990},"apc_paid":{"value":2390,"currency":"EUR","value_usd":2990},"fwci":6.3817,"has_fulltext":true,"cited_by_count":59,"citation_normalized_percentile":{"value":0.97518286,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":100},"biblio":{"volume":"34","issue":"5","first_page":"1336","last_page":"1362"},"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9904999732971191,"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"}},{"id":"https://openalex.org/T11270","display_name":"Complex Systems and Time Series Analysis","score":0.9599000215530396,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7247200012207031},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6881774663925171},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5664249062538147},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.56102454662323},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5467511415481567},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5289706587791443},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.47510039806365967},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3775317072868347},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3447352647781372}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7247200012207031},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6881774663925171},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5664249062538147},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.56102454662323},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5467511415481567},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5289706587791443},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.47510039806365967},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3775317072868347},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3447352647781372},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"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/s10618-020-00690-z","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10618-020-00690-z","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10618-020-00690-z.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","license_id":"https://openalex.org/licenses/cc-by","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/s10618-020-00690-z","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10618-020-00690-z","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10618-020-00690-z.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","license_id":"https://openalex.org/licenses/cc-by","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/7","score":0.44999998807907104,"display_name":"Affordable and clean energy"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320323700","display_name":"Humboldt-Universit\u00e4t zu Berlin","ror":"https://ror.org/01hcx6992"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3036081019.pdf","grobid_xml":"https://content.openalex.org/works/W3036081019.grobid-xml"},"referenced_works_count":47,"referenced_works":["https://openalex.org/W166510246","https://openalex.org/W1487019984","https://openalex.org/W1565746575","https://openalex.org/W1823976324","https://openalex.org/W1900747440","https://openalex.org/W1968354112","https://openalex.org/W1975257359","https://openalex.org/W1978371851","https://openalex.org/W2002828701","https://openalex.org/W2011208599","https://openalex.org/W2011659384","https://openalex.org/W2016944175","https://openalex.org/W2020517213","https://openalex.org/W2035104901","https://openalex.org/W2045938006","https://openalex.org/W2050493487","https://openalex.org/W2078765398","https://openalex.org/W2098899567","https://openalex.org/W2099302229","https://openalex.org/W2118585731","https://openalex.org/W2119939946","https://openalex.org/W2123007178","https://openalex.org/W2123502857","https://openalex.org/W2124519329","https://openalex.org/W2132870739","https://openalex.org/W2141536962","https://openalex.org/W2153635508","https://openalex.org/W2295972917","https://openalex.org/W2335814575","https://openalex.org/W2342546612","https://openalex.org/W2403962807","https://openalex.org/W2404243041","https://openalex.org/W2461572269","https://openalex.org/W2524083015","https://openalex.org/W2551393996","https://openalex.org/W2581867724","https://openalex.org/W2593973948","https://openalex.org/W2762410434","https://openalex.org/W2765566334","https://openalex.org/W2888791883","https://openalex.org/W2915399950","https://openalex.org/W2917732394","https://openalex.org/W2940205355","https://openalex.org/W2946507061","https://openalex.org/W2963798004","https://openalex.org/W3098918569","https://openalex.org/W3098967488"],"related_works":["https://openalex.org/W2378211422","https://openalex.org/W4321353415","https://openalex.org/W2130974462","https://openalex.org/W972276598","https://openalex.org/W2086519370","https://openalex.org/W2622688551","https://openalex.org/W2119012848","https://openalex.org/W1990205660","https://openalex.org/W1550175370","https://openalex.org/W4387331850"],"abstract_inverted_index":{"Abstract":[0],"Early":[1],"time":[2,12,41,64,96,107,133,141,167],"series":[3,13,42,101,168],"classification":[4,58,68,74,155,231],"(eTSC)":[5],"is":[6,33,69,192,212],"the":[7,21,57,63,103,131,158,165,189,225],"problem":[8,59],"of":[9,29,39,126,202],"classifying":[10],"a":[11,40,48,67,92,111,127,136,146,153,161,184,199],"after":[14,198],"as":[15,18,152,180],"few":[16],"measurements":[17,118],"possible":[19,23],"with":[20,78],"highest":[22],"accuracy.":[24,86,232],"The":[25,87],"most":[26],"critical":[27],"issue":[28],"any":[30],"eTSC":[31,89,151],"method":[32],"to":[34,46,76,84,169,214],"decide":[35],"when":[36],"enough":[37],"data":[38,53],"has":[43,75,102],"been":[44],"seen":[45],"take":[47],"decision:":[49],"Waiting":[50],"for":[51,134],"more":[52],"points":[54],"usually":[55],"makes":[56],"easier":[60],"but":[61],"delays":[62],"in":[65,71,114],"which":[66,195],"made;":[70],"contrast,":[72],"earlier":[73,217],"cope":[77],"less":[79],"input":[80],"data,":[81],"often":[82],"leading":[83],"inferior":[85],"state-of-the-art":[88],"methods":[90],"compute":[91,170],"fixed":[93],"optimal":[94],"decision":[95,137],"assuming":[97],"that":[98,130,149,188],"every":[99],"times":[100,122,216],"same":[104,226],"defined":[105],"start":[106,119],"(like":[108,123],"turning":[109],"on":[110],"machine).":[112],"However,":[113,173],"many":[115],"real-life":[116,240],"applications":[117],"at":[120,218],"arbitrary":[121],"measuring":[124],"heartbeats":[125],"patient),":[128],"implying":[129],"best":[132],"taking":[135],"varies":[138],"widely":[139],"between":[140],"series.":[142],"We":[143,233],"present":[144],"TEASER,":[145],"novel":[147],"algorithm":[148],"models":[150],"two-tier":[154],"problem:":[156],"In":[157,204],"first":[159],"tier,":[160],"classifier":[162,186],"periodically":[163],"assesses":[164],"incoming":[166],"class":[171,175],"probabilities.":[172],"these":[174],"probabilities":[176],"are":[177],"only":[178],"used":[179],"output":[181],"label":[182,191],"if":[183],"second-tier":[185],"decides":[187],"predicted":[190],"reliable":[193],"enough,":[194],"can":[196],"happen":[197],"different":[200],"number":[201],"measurements.":[203],"an":[205,228],"evaluation":[206],"using":[207,239],"45":[208],"benchmark":[209],"datasets,":[210],"TEASER":[211],"two":[213],"three":[215],"predictions":[219],"than":[220],"its":[221],"competitors":[222],"while":[223],"reaching":[224],"or":[227],"even":[229],"higher":[230],"further":[234],"show":[235],"TEASER\u2019s":[236],"superior":[237],"performance":[238],"use":[241],"cases,":[242],"namely":[243],"energy":[244],"monitoring,":[245],"and":[246],"gait":[247],"detection.":[248]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":8},{"year":2023,"cited_by_count":11},{"year":2022,"cited_by_count":18},{"year":2021,"cited_by_count":12},{"year":2020,"cited_by_count":1}],"updated_date":"2026-05-21T09:19:25.381259","created_date":"2025-10-10T00:00:00"}
