{"id":"https://openalex.org/W4205510591","doi":"https://doi.org/10.1145/3493700.3493711","title":"Time Series Representation Learning with Contrastive Triplet Selection","display_name":"Time Series Representation Learning with Contrastive Triplet Selection","publication_year":2022,"publication_date":"2022-01-07","ids":{"openalex":"https://openalex.org/W4205510591","doi":"https://doi.org/10.1145/3493700.3493711"},"language":"en","primary_location":{"id":"doi:10.1145/3493700.3493711","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3493700.3493711","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 5th Joint International Conference on Data Science &amp; Management of Data (9th ACM IKDD CODS and 27th COMAD)","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/A5100914884","display_name":"Yuan\u2010Chi Chang","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Yuan-Chi Chang","raw_affiliation_strings":["IBM Research AI, US"],"affiliations":[{"raw_affiliation_string":"IBM Research AI, US","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052501780","display_name":"Dharmashankar Subramanian","orcid":"https://orcid.org/0000-0002-1990-7740"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dharmashankar Subramanian","raw_affiliation_strings":["IBM Research AI, US"],"affiliations":[{"raw_affiliation_string":"IBM Research AI, US","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087072038","display_name":"Raju Pavuluri","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Raju Pavuluri","raw_affiliation_strings":["IBM Research AI, US"],"affiliations":[{"raw_affiliation_string":"IBM Research AI, US","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5000162282","display_name":"Timothy Dinger","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Timothy Dinger","raw_affiliation_strings":["IBM Research AI, US"],"affiliations":[{"raw_affiliation_string":"IBM Research AI, US","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100914884"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.5944,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.61495028,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"1","issue":null,"first_page":"46","last_page":"53"},"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9950000047683716,"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/T11309","display_name":"Music and Audio Processing","score":0.9865000247955322,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6856372952461243},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.6818417906761169},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.6165494322776794},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5326561331748962},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.530331015586853},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.46809032559394836},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.0774247944355011},{"id":"https://openalex.org/keywords/paleontology","display_name":"Paleontology","score":0.056886255741119385}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6856372952461243},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.6818417906761169},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.6165494322776794},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5326561331748962},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.530331015586853},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.46809032559394836},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0774247944355011},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.056886255741119385},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3493700.3493711","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3493700.3493711","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 5th Joint International Conference on Data Science &amp; Management of Data (9th ACM IKDD CODS and 27th COMAD)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":8,"referenced_works":["https://openalex.org/W2096733369","https://openalex.org/W2595551253","https://openalex.org/W2606611007","https://openalex.org/W2757662681","https://openalex.org/W2786161686","https://openalex.org/W2999767092","https://openalex.org/W3034014386","https://openalex.org/W4301172471"],"related_works":["https://openalex.org/W2611614995","https://openalex.org/W2368651715","https://openalex.org/W2789919619","https://openalex.org/W1552159754","https://openalex.org/W2148757832","https://openalex.org/W4321496520","https://openalex.org/W2293457016","https://openalex.org/W2131420137","https://openalex.org/W2606073172","https://openalex.org/W3107474891"],"abstract_inverted_index":{"Representation":[0],"learning,":[1],"with":[2,125],"its":[3,118],"proven":[4],"appeal":[5],"and":[6,10,43,47,83,116,137,149],"advantage":[7,159],"in":[8,86,94,134],"visual":[9],"textual":[11],"modalities,":[12],"has":[13],"seen":[14],"extensions":[15],"to":[16,32,59,79,110,114,120,129],"numerical":[17,55],"time":[18,39,56],"series.":[19,40],"Recent":[20],"work":[21],"proposed":[22],"a":[23,34,105],"triplet":[24,30,72,144],"loss":[25],"formulation":[26],"based":[27,65],"on":[28,66],"random":[29,143],"sampling":[31,154],"derive":[33],"fixed":[35],"length":[36],"embedding":[37],"for":[38],"Unlike":[41],"images":[42],"text":[44],"however,":[45],"statistical":[46],"distance":[48,113,119],"measures":[49],"can":[50],"be":[51],"readily":[52],"computed":[53],"from":[54],"series":[57],"data":[58],"quantitatively":[60],"contrast":[61],"differences":[62],"or":[63],"cluster":[64],"similarity.":[67],"This":[68],"paper":[69],"investigates":[70],"the":[71,87,95],"mining":[73],"problem":[74],"through":[75],"contrastive":[76],"identification":[77],"methods":[78,132],"select":[80],"anchors,":[81],"positive":[82],"negative":[84],"samples":[85],"raw":[88],"signal":[89],"space":[90],"as":[91,93],"well":[92],"embedded":[96],"vector":[97],"space.":[98],"Selected":[99],"triplets":[100],"are":[101],"then":[102],"learned":[103],"by":[104],"causal":[106],"temporal":[107],"neural":[108],"network":[109],"minimize":[111],"anchor\u2019s":[112],"positives":[115],"maximize":[117],"negatives.":[121],"Experimental":[122],"results":[123],"along":[124],"an":[126],"ablation":[127],"study":[128],"compare":[130],"these":[131],"measured":[133],"classification":[135],"accuracy":[136],"variance":[138],"demonstrated":[139],"notable":[140],"improvement":[141,152],"over":[142],"selection.":[145],"We":[146],"also":[147],"investigate":[148],"report":[150],"performance":[151],"when":[153],"avoids":[155],"label":[156],"contamination,":[157],"demonstrating":[158],"of":[160],"algorithms":[161],"proposed.":[162]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
