{"id":"https://openalex.org/W2923846981","doi":"https://doi.org/10.1109/icassp.2019.8682322","title":"Trainable Time Warping: Aligning Time-series in the Continuous-time Domain","display_name":"Trainable Time Warping: Aligning Time-series in the Continuous-time Domain","publication_year":2019,"publication_date":"2019-04-17","ids":{"openalex":"https://openalex.org/W2923846981","doi":"https://doi.org/10.1109/icassp.2019.8682322","mag":"2923846981"},"language":"en","primary_location":{"id":"doi:10.1109/icassp.2019.8682322","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2019.8682322","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5025206583","display_name":"Soheil Khorram","orcid":"https://orcid.org/0000-0002-2306-3341"},"institutions":[{"id":"https://openalex.org/I27837315","display_name":"University of Michigan\u2013Ann Arbor","ror":"https://ror.org/00jmfr291","country_code":"US","type":"education","lineage":["https://openalex.org/I27837315"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Soheil Khorram","raw_affiliation_strings":["University of Michigan, Ann Arbor, USA"],"affiliations":[{"raw_affiliation_string":"University of Michigan, Ann Arbor, USA","institution_ids":["https://openalex.org/I27837315"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013242137","display_name":"Melvin G. McInnis","orcid":"https://orcid.org/0000-0002-0375-6247"},"institutions":[{"id":"https://openalex.org/I27837315","display_name":"University of Michigan\u2013Ann Arbor","ror":"https://ror.org/00jmfr291","country_code":"US","type":"education","lineage":["https://openalex.org/I27837315"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Melvin G McInnis","raw_affiliation_strings":["University of Michigan, Ann Arbor, USA"],"affiliations":[{"raw_affiliation_string":"University of Michigan, Ann Arbor, USA","institution_ids":["https://openalex.org/I27837315"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5003136334","display_name":"Emily Mower Provost","orcid":"https://orcid.org/0000-0003-1870-6063"},"institutions":[{"id":"https://openalex.org/I27837315","display_name":"University of Michigan\u2013Ann Arbor","ror":"https://ror.org/00jmfr291","country_code":"US","type":"education","lineage":["https://openalex.org/I27837315"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Emily Mower Provost","raw_affiliation_strings":["University of Michigan, Ann Arbor, USA"],"affiliations":[{"raw_affiliation_string":"University of Michigan, Ann Arbor, USA","institution_ids":["https://openalex.org/I27837315"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5025206583"],"corresponding_institution_ids":["https://openalex.org/I27837315"],"apc_list":null,"apc_paid":null,"fwci":1.3409,"has_fulltext":false,"cited_by_count":16,"citation_normalized_percentile":{"value":0.80784481,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"3502","last_page":"3506"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9998999834060669,"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":0.9998999834060669,"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/T11309","display_name":"Music and Audio Processing","score":0.993399977684021,"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/T13083","display_name":"Advanced Text Analysis Techniques","score":0.948199987411499,"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/dynamic-time-warping","display_name":"Dynamic time warping","score":0.9060465097427368},{"id":"https://openalex.org/keywords/image-warping","display_name":"Image warping","score":0.6502384543418884},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.6492729187011719},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6355981826782227},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.6054644584655762},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.578248143196106},{"id":"https://openalex.org/keywords/time-domain","display_name":"Time domain","score":0.5511755347251892},{"id":"https://openalex.org/keywords/computational-complexity-theory","display_name":"Computational complexity theory","score":0.4822962284088135},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.46161210536956787},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4572240710258484},{"id":"https://openalex.org/keywords/time-complexity","display_name":"Time complexity","score":0.4460163116455078},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.420485258102417},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4132058918476105},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2986357510089874},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.19910427927970886},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.15688273310661316}],"concepts":[{"id":"https://openalex.org/C88516994","wikidata":"https://www.wikidata.org/wiki/Q1268863","display_name":"Dynamic time warping","level":2,"score":0.9060465097427368},{"id":"https://openalex.org/C157202957","wikidata":"https://www.wikidata.org/wiki/Q1659609","display_name":"Image warping","level":2,"score":0.6502384543418884},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.6492729187011719},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6355981826782227},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.6054644584655762},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.578248143196106},{"id":"https://openalex.org/C103824480","wikidata":"https://www.wikidata.org/wiki/Q185889","display_name":"Time domain","level":2,"score":0.5511755347251892},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.4822962284088135},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.46161210536956787},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4572240710258484},{"id":"https://openalex.org/C311688","wikidata":"https://www.wikidata.org/wiki/Q2393193","display_name":"Time complexity","level":2,"score":0.4460163116455078},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.420485258102417},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4132058918476105},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2986357510089874},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.19910427927970886},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.15688273310661316},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","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/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp.2019.8682322","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2019.8682322","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320332550","display_name":"University of California, Riverside","ror":"https://ror.org/03nawhv43"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W116137555","https://openalex.org/W1522301498","https://openalex.org/W1551040145","https://openalex.org/W1637570796","https://openalex.org/W1732493212","https://openalex.org/W1785074626","https://openalex.org/W1963540633","https://openalex.org/W2063411627","https://openalex.org/W2084616221","https://openalex.org/W2091921805","https://openalex.org/W2097909140","https://openalex.org/W2120440617","https://openalex.org/W2132585078","https://openalex.org/W2144994235","https://openalex.org/W2148615159","https://openalex.org/W2150696241","https://openalex.org/W2153964958","https://openalex.org/W2156301828","https://openalex.org/W2158301611","https://openalex.org/W2183118770","https://openalex.org/W2581490870","https://openalex.org/W2583148033","https://openalex.org/W2597759521","https://openalex.org/W2614356544","https://openalex.org/W2747981714","https://openalex.org/W2750483899","https://openalex.org/W2910738288","https://openalex.org/W2963405869","https://openalex.org/W2964121744","https://openalex.org/W3046555099","https://openalex.org/W6604704062","https://openalex.org/W6631190155","https://openalex.org/W6636885848","https://openalex.org/W6682511423","https://openalex.org/W6734312481","https://openalex.org/W6737941110","https://openalex.org/W6781021684"],"related_works":["https://openalex.org/W2347413598","https://openalex.org/W1918542373","https://openalex.org/W71572444","https://openalex.org/W1997383766","https://openalex.org/W2154472250","https://openalex.org/W2350336482","https://openalex.org/W2229352698","https://openalex.org/W2104719785","https://openalex.org/W1589381382","https://openalex.org/W961142965"],"abstract_inverted_index":{"DTW":[0],"calculates":[1],"the":[2,20,34,90,93,101,134,137,143,146],"similarity":[3],"or":[4],"alignment":[5,76,99],"between":[6],"two":[7],"signals,":[8],"subject":[9],"to":[10],"temporal":[11],"warping.":[12],"However,":[13],"its":[14],"computational":[15,55],"complexity":[16,85],"grows":[17],"exponentially":[18],"with":[19],"number":[21,35,91],"of":[22,36,95,133,142],"time-series.":[23,96],"Although":[24],"there":[25],"have":[26],"been":[27],"algorithms":[28],"developed":[29],"that":[30],"are":[31,39],"linear":[32,54,87],"in":[33,42,88,100,123],"time-series,":[37],"they":[38],"generally":[40],"quadratic":[41],"time-series":[43,124],"length.":[44],"The":[45],"exception":[46],"is":[47,67,86],"generalized":[48],"time":[49,63,81],"warping":[50,64,82],"(GTW),":[51],"which":[52],"has":[53],"cost.":[56],"Yet,":[57],"it":[58],"can":[59],"only":[60],"identify":[61],"simple":[62],"functions.":[65],"There":[66],"a":[68,71,105,110],"need":[69],"for":[70,136,145],"new":[72],"fast,":[73],"high-quality":[74],"multisequence":[75],"algorithm.":[77],"We":[78,114],"introduce":[79],"trainable":[80],"(TTW),":[83],"whose":[84],"both":[89],"and":[92,109,117,126,140],"length":[94],"TTW":[97,116,128],"performs":[98],"continuoustime":[102],"domain":[103],"using":[104],"sinc":[106],"convolutional":[107],"kernel":[108],"gradient-based":[111],"optimization":[112],"technique.":[113],"compare":[115],"GTW":[118,130],"on":[119,131],"S5":[120],"UCR":[121],"datasets":[122,135,144],"averaging":[125,138],"classification.":[127],"outperforms":[129],"67.1%":[132],"tasks,":[139],"61.2%":[141],"classification":[147],"tasks.":[148]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":2}],"updated_date":"2026-03-31T07:56:22.981413","created_date":"2025-10-10T00:00:00"}
