{"id":"https://openalex.org/W4396822597","doi":"https://doi.org/10.1145/3637528.3671673","title":"Self-Supervised Learning of Time Series Representation via Diffusion Process and Imputation-Interpolation-Forecasting Mask","display_name":"Self-Supervised Learning of Time Series Representation via Diffusion Process and Imputation-Interpolation-Forecasting Mask","publication_year":2024,"publication_date":"2024-08-24","ids":{"openalex":"https://openalex.org/W4396822597","doi":"https://doi.org/10.1145/3637528.3671673"},"language":"en","primary_location":{"id":"doi:10.1145/3637528.3671673","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3637528.3671673","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3637528.3671673","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3637528.3671673","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5094008624","display_name":"Zineb Senane","orcid":"https://orcid.org/0009-0001-6451-0136"},"institutions":[{"id":"https://openalex.org/I86987016","display_name":"KTH Royal Institute of Technology","ror":"https://ror.org/026vcq606","country_code":"SE","type":"education","lineage":["https://openalex.org/I86987016"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Zineb Senane","raw_affiliation_strings":["Motherbrain, EQT Group &amp; KTH Royal Institute of Technology, Stockholm, Sweden"],"raw_orcid":"https://orcid.org/0009-0001-6451-0136","affiliations":[{"raw_affiliation_string":"Motherbrain, EQT Group &amp; KTH Royal Institute of Technology, Stockholm, Sweden","institution_ids":["https://openalex.org/I86987016"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090184412","display_name":"Lele Cao","orcid":"https://orcid.org/0000-0002-5680-9031"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lele Cao","raw_affiliation_strings":["Motherbrain, EQT Group, Stockholm, Sweden"],"raw_orcid":"https://orcid.org/0000-0002-5680-9031","affiliations":[{"raw_affiliation_string":"Motherbrain, EQT Group, Stockholm, Sweden","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041333739","display_name":"Valentin Leonhard Buchner","orcid":"https://orcid.org/0000-0002-1262-3016"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Valentin Leonhard Buchner","raw_affiliation_strings":["Motherbrain, EQT Group, Stockholm, Sweden"],"raw_orcid":"https://orcid.org/0000-0002-1262-3016","affiliations":[{"raw_affiliation_string":"Motherbrain, EQT Group, Stockholm, Sweden","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058739456","display_name":"Yusuke Tashiro","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yusuke Tashiro","raw_affiliation_strings":["Mitsubishi UFJ Trust Investment Technology Institute, Tokyo, Japan"],"raw_orcid":"https://orcid.org/0009-0000-2659-7122","affiliations":[{"raw_affiliation_string":"Mitsubishi UFJ Trust Investment Technology Institute, Tokyo, Japan","institution_ids":[]}]},{"author_position":"middle","author":{"id":null,"display_name":"Lei You","orcid":"https://orcid.org/0000-0002-4741-0715"},"institutions":[{"id":"https://openalex.org/I96673099","display_name":"Technical University of Denmark","ror":"https://ror.org/04qtj9h94","country_code":"DK","type":"education","lineage":["https://openalex.org/I96673099"]}],"countries":["DK"],"is_corresponding":false,"raw_author_name":"Lei You","raw_affiliation_strings":["Technical University of Denmark, Ballerup, Denmark"],"raw_orcid":"https://orcid.org/0000-0002-4741-0715","affiliations":[{"raw_affiliation_string":"Technical University of Denmark, Ballerup, Denmark","institution_ids":["https://openalex.org/I96673099"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039027821","display_name":"Pawel Herman","orcid":"https://orcid.org/0000-0001-6553-823X"},"institutions":[{"id":"https://openalex.org/I86987016","display_name":"KTH Royal Institute of Technology","ror":"https://ror.org/026vcq606","country_code":"SE","type":"education","lineage":["https://openalex.org/I86987016"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Pawel Andrzej Herman","raw_affiliation_strings":["KTH Royal Institute of Technology, Stockholm, Sweden"],"raw_orcid":"https://orcid.org/0000-0001-6553-823X","affiliations":[{"raw_affiliation_string":"KTH Royal Institute of Technology, Stockholm, Sweden","institution_ids":["https://openalex.org/I86987016"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080608277","display_name":"Mats G. Nordahl","orcid":null},"institutions":[{"id":"https://openalex.org/I86987016","display_name":"KTH Royal Institute of Technology","ror":"https://ror.org/026vcq606","country_code":"SE","type":"education","lineage":["https://openalex.org/I86987016"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Mats Nordahl","raw_affiliation_strings":["KTH Royal Institute of Technology, Stockholm, Sweden"],"raw_orcid":"https://orcid.org/0009-0000-4347-5928","affiliations":[{"raw_affiliation_string":"KTH Royal Institute of Technology, Stockholm, Sweden","institution_ids":["https://openalex.org/I86987016"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053420346","display_name":"Ruibo Tu","orcid":"https://orcid.org/0000-0003-1356-9653"},"institutions":[{"id":"https://openalex.org/I86987016","display_name":"KTH Royal Institute of Technology","ror":"https://ror.org/026vcq606","country_code":"SE","type":"education","lineage":["https://openalex.org/I86987016"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Ruibo Tu","raw_affiliation_strings":["KTH Royal Institute of Technology, Stockholm, Sweden"],"raw_orcid":"https://orcid.org/0000-0003-1356-9653","affiliations":[{"raw_affiliation_string":"KTH Royal Institute of Technology, Stockholm, Sweden","institution_ids":["https://openalex.org/I86987016"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5052213727","display_name":"Vilhelm von Ehrenheim","orcid":"https://orcid.org/0000-0002-4210-4989"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Vilhelm von Ehrenheim","raw_affiliation_strings":["Motherbrain, EQT Group &amp; QA.tech, Stockholm, Sweden"],"raw_orcid":"https://orcid.org/0000-0002-4210-4989","affiliations":[{"raw_affiliation_string":"Motherbrain, EQT Group &amp; QA.tech, Stockholm, Sweden","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":22,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"2560","last_page":"2571"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9003999829292297,"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"}},"topics":[{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9003999829292297,"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/imputation","display_name":"Imputation (statistics)","score":0.6679180860519409},{"id":"https://openalex.org/keywords/interpolation","display_name":"Interpolation (computer graphics)","score":0.6118264198303223},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5927287340164185},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5743222832679749},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.5542289614677429},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5288687348365784},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5273502469062805},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4677819013595581},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3868817687034607},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.34360283613204956},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.32881057262420654},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2683830261230469},{"id":"https://openalex.org/keywords/missing-data","display_name":"Missing data","score":0.21722868084907532}],"concepts":[{"id":"https://openalex.org/C58041806","wikidata":"https://www.wikidata.org/wiki/Q1660484","display_name":"Imputation (statistics)","level":3,"score":0.6679180860519409},{"id":"https://openalex.org/C137800194","wikidata":"https://www.wikidata.org/wiki/Q11713455","display_name":"Interpolation (computer graphics)","level":3,"score":0.6118264198303223},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5927287340164185},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5743222832679749},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.5542289614677429},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5288687348365784},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5273502469062805},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4677819013595581},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3868817687034607},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.34360283613204956},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.32881057262420654},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2683830261230469},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.21722868084907532},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"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/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1145/3637528.3671673","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3637528.3671673","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3637528.3671673","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2405.05959","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2405.05959","pdf_url":"https://arxiv.org/pdf/2405.05959","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"pmh:oai:pure.atira.dk:publications/77585d85-8200-4595-aa9f-6ded73fc5e64","is_oa":true,"landing_page_url":"https://orbit.dtu.dk/en/publications/77585d85-8200-4595-aa9f-6ded73fc5e64","pdf_url":null,"source":{"id":"https://openalex.org/S4306400705","display_name":"Technical University of Denmark, DTU Orbit (Technical University of Denmark, DTU)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I96673099","host_organization_name":"Technical University of Denmark","host_organization_lineage":["https://openalex.org/I96673099"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Senane , Z , Cao , L , Buchner , V L , Tashiro , Y , You , L , Herman , P , Nordahl , M , Tu , R &amp; Von Ehrenheim , V 2024 , Self-Supervised Learning of Time Series Representation via Diffusion Process and Imputation-Interpolation-Forecasting Mask . in Proceedings of KDD \u201924 . Association for Computing Machinery , pp. 2560-2571 , ACM KDD 2024 , Barcelona , Spain , 25/08/2024 . https://doi.org/10.1145/3637528.3671673","raw_type":"contributionToPeriodical"}],"best_oa_location":{"id":"doi:10.1145/3637528.3671673","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3637528.3671673","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3637528.3671673","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G8772767756","display_name":null,"funder_award_id":"20023495","funder_id":"https://openalex.org/F4320321681","funder_display_name":"Ministry of Trade, Industry and Energy"}],"funders":[{"id":"https://openalex.org/F4320321681","display_name":"Ministry of Trade, Industry and Energy","ror":"https://ror.org/008nkqk13"}],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4396822597.pdf"},"referenced_works_count":36,"referenced_works":["https://openalex.org/W1502922572","https://openalex.org/W1522684182","https://openalex.org/W2033403400","https://openalex.org/W2407991977","https://openalex.org/W2730106296","https://openalex.org/W2786827964","https://openalex.org/W2883725317","https://openalex.org/W2889326414","https://openalex.org/W2950361482","https://openalex.org/W2964010366","https://openalex.org/W2971043720","https://openalex.org/W2973049979","https://openalex.org/W2997653844","https://openalex.org/W3000500483","https://openalex.org/W3023371261","https://openalex.org/W3035623224","https://openalex.org/W3087040032","https://openalex.org/W3121975202","https://openalex.org/W3167749434","https://openalex.org/W3170981104","https://openalex.org/W3171007011","https://openalex.org/W3171153522","https://openalex.org/W3177318507","https://openalex.org/W3181975995","https://openalex.org/W3188872815","https://openalex.org/W3190152617","https://openalex.org/W3193597430","https://openalex.org/W3199148273","https://openalex.org/W3207924272","https://openalex.org/W4221108754","https://openalex.org/W4235169531","https://openalex.org/W4290877088","https://openalex.org/W4293255441","https://openalex.org/W4319335604","https://openalex.org/W4382203079","https://openalex.org/W4382317959"],"related_works":["https://openalex.org/W4211215373","https://openalex.org/W3217094455","https://openalex.org/W2989589450","https://openalex.org/W3119637569","https://openalex.org/W2405773734","https://openalex.org/W2791189374","https://openalex.org/W3123325766","https://openalex.org/W2622688551","https://openalex.org/W1550175370","https://openalex.org/W1990205660"],"abstract_inverted_index":{"Time":[0,12,78],"Series":[1,13,79],"Representation":[2],"Learning":[3,19],"(TSRL)":[4],"focuses":[5],"on":[6,132],"generating":[7],"informative":[8],"representations":[9,179],"for":[10,73],"various":[11],"(TS)":[14],"modeling":[15],"tasks.":[16],"Traditional":[17],"Self-Supervised":[18],"(SSL)":[20],"methods":[21,49],"in":[22,69,149,177],"TSRL":[23,91],"fall":[24],"into":[25,97],"four":[26],"main":[27],"categories:":[28],"reconstructive,":[29],"adversarial,":[30],"contrastive,":[31],"and":[32,43,64,99,156,167,175],"predictive,":[33],"each":[34],"with":[35,117],"a":[36,67,109,118,127],"common":[37],"challenge":[38],"of":[39,180],"sensitivity":[40],"to":[41,121,136,140],"noise":[42,138],"intricate":[44],"data":[45,96],"nuances.":[46],"Recently,":[47],"diffusion-based":[48,89],"have":[50],"shown":[51],"advanced":[52],"generative":[53],"capabilities.":[54],"However,":[55],"they":[56],"primarily":[57],"target":[58],"specific":[59],"application":[60],"scenarios":[61],"like":[62],"imputation":[63],"forecasting,":[65,152],"leaving":[66],"gap":[68,85],"leveraging":[70],"diffusion":[71,129],"models":[72],"generic":[74],"TSRL.":[75],"Our":[76],"work,":[77],"Diffusion":[80],"Embedding":[81],"(TSDE),":[82],"bridges":[83],"this":[84],"as":[86],"the":[87,122,133,141],"first":[88],"SSL":[90],"approach.":[92],"TSDE":[93],"segments":[94],"TS":[95,181],"observed":[98,123],"masked":[100,142],"parts":[101],"using":[102],"an":[103,161],"Imputation-Interpolation-Forecasting":[104],"(IIF)":[105],"mask.":[106],"It":[107],"applies":[108],"trainable":[110],"embedding":[111,165],"function,":[112],"featuring":[113],"dual-orthogonal":[114],"Transformer":[115],"encoders":[116],"crossover":[119],"mechanism,":[120],"part.":[124,143],"We":[125,158],"train":[126],"reverse":[128],"process":[130],"conditioned":[131],"embeddings,":[134],"designed":[135],"predict":[137],"added":[139],"Extensive":[144],"experiments":[145],"demonstrate":[146],"TSDE's":[147,173],"superiority":[148],"imputation,":[150],"interpolation,":[151],"anomaly":[153],"detection,":[154],"classification,":[155],"clustering.":[157],"also":[159],"conduct":[160],"ablation":[162],"study,":[163],"present":[164],"visualizations,":[166],"compare":[168],"inference":[169],"speed,":[170],"further":[171],"substantiating":[172],"efficiency":[174],"validity":[176],"learning":[178],"data.":[182]},"counts_by_year":[{"year":2026,"cited_by_count":8},{"year":2025,"cited_by_count":13},{"year":2024,"cited_by_count":1}],"updated_date":"2026-07-17T09:13:05.818461","created_date":"2025-10-10T00:00:00"}
