{"id":"https://openalex.org/W4289258409","doi":"https://doi.org/10.1145/3219819.3220102","title":"Deep Multi-Output Forecasting","display_name":"Deep Multi-Output Forecasting","publication_year":2018,"publication_date":"2018-07-19","ids":{"openalex":"https://openalex.org/W4289258409","doi":"https://doi.org/10.1145/3219819.3220102"},"language":"en","primary_location":{"id":"doi:10.1145/3219819.3220102","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3219819.3220102","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3219819.3220102","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3219819.3220102","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5111567645","display_name":"Ian Fox","orcid":null},"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":"Ian Fox","raw_affiliation_strings":["University of Michigan, Ann Arbor, MI, USA"],"affiliations":[{"raw_affiliation_string":"University of Michigan, Ann Arbor, MI, USA","institution_ids":["https://openalex.org/I27837315"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103191357","display_name":"Lynn Ang","orcid":"https://orcid.org/0000-0001-7133-9886"},"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":"Lynn Ang","raw_affiliation_strings":["University of Michigan, Ann Arbor, MI, USA"],"affiliations":[{"raw_affiliation_string":"University of Michigan, Ann Arbor, MI, USA","institution_ids":["https://openalex.org/I27837315"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083615688","display_name":"Mamta Jaiswal","orcid":"https://orcid.org/0000-0003-1727-6225"},"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":"Mamta Jaiswal","raw_affiliation_strings":["University of Michigan, Ann Arbor, MI, USA"],"affiliations":[{"raw_affiliation_string":"University of Michigan, Ann Arbor, MI, USA","institution_ids":["https://openalex.org/I27837315"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018561083","display_name":"Rodica Pop\u2010Busui","orcid":"https://orcid.org/0000-0002-2042-1350"},"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":"Rodica Pop-Busui","raw_affiliation_strings":["University of Michigan, Ann Arbor, MI, USA"],"affiliations":[{"raw_affiliation_string":"University of Michigan, Ann Arbor, MI, USA","institution_ids":["https://openalex.org/I27837315"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5055037967","display_name":"Jenna Wiens","orcid":"https://orcid.org/0000-0002-1057-7722"},"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":"Jenna Wiens","raw_affiliation_strings":["University of Michigan, Ann Arbor, MI, USA"],"affiliations":[{"raw_affiliation_string":"University of Michigan, Ann Arbor, MI, USA","institution_ids":["https://openalex.org/I27837315"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5111567645"],"corresponding_institution_ids":["https://openalex.org/I27837315"],"apc_list":null,"apc_paid":null,"fwci":3.9062,"has_fulltext":true,"cited_by_count":58,"citation_normalized_percentile":{"value":0.94759641,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1387","last_page":"1395"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10560","display_name":"Diabetes Management and Research","score":0.9936000108718872,"subfield":{"id":"https://openalex.org/subfields/2712","display_name":"Endocrinology, Diabetes and Metabolism"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T10560","display_name":"Diabetes Management and Research","score":0.9936000108718872,"subfield":{"id":"https://openalex.org/subfields/2712","display_name":"Endocrinology, Diabetes and Metabolism"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10745","display_name":"Heart Rate Variability and Autonomic Control","score":0.9825000166893005,"subfield":{"id":"https://openalex.org/subfields/2705","display_name":"Cardiology and Cardiovascular Medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10027","display_name":"Diabetes, Cardiovascular Risks, and Lipoproteins","score":0.9710000157356262,"subfield":{"id":"https://openalex.org/subfields/2712","display_name":"Endocrinology, Diabetes and Metabolism"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7322879433631897},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.5672924518585205},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.546096682548523},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5220298767089844},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5204291939735413},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4919383227825165},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.47843530774116516},{"id":"https://openalex.org/keywords/signal","display_name":"SIGNAL (programming language)","score":0.46498602628707886},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.44742682576179504},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.43553104996681213},{"id":"https://openalex.org/keywords/mean-squared-prediction-error","display_name":"Mean squared prediction error","score":0.431885302066803},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.4217662513256073},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.38580507040023804},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.08623123168945312}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7322879433631897},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.5672924518585205},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.546096682548523},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5220298767089844},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5204291939735413},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4919383227825165},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.47843530774116516},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.46498602628707886},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.44742682576179504},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.43553104996681213},{"id":"https://openalex.org/C167085575","wikidata":"https://www.wikidata.org/wiki/Q6803654","display_name":"Mean squared prediction error","level":2,"score":0.431885302066803},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.4217662513256073},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.38580507040023804},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.08623123168945312},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","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/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","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},{"id":"https://openalex.org/C111368507","wikidata":"https://www.wikidata.org/wiki/Q43518","display_name":"Oceanography","level":1,"score":0.0},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3219819.3220102","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3219819.3220102","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3219819.3220102","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1806.05357","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1806.05357","pdf_url":"https://arxiv.org/pdf/1806.05357","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"doi:10.1145/3219819.3220102","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3219819.3220102","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3219819.3220102","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1442248903","display_name":null,"funder_award_id":"HL102334","funder_id":"https://openalex.org/F4320332161","funder_display_name":"National Institutes of Health"},{"id":"https://openalex.org/G3298056745","display_name":null,"funder_award_id":"R01HL","funder_id":"https://openalex.org/F4320337338","funder_display_name":"National Heart, Lung, and Blood Institute"},{"id":"https://openalex.org/G3530239307","display_name":null,"funder_award_id":"NHLBI","funder_id":"https://openalex.org/F4320332161","funder_display_name":"National Institutes of Health"},{"id":"https://openalex.org/G7643161287","display_name":null,"funder_award_id":"NIH/NHLBI","funder_id":"https://openalex.org/F4320337338","funder_display_name":"National Heart, Lung, and Blood Institute"},{"id":"https://openalex.org/G848032724","display_name":null,"funder_award_id":"Science","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G8749050902","display_name":null,"funder_award_id":"IIS-1553146","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"},{"id":"https://openalex.org/F4320332161","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88"},{"id":"https://openalex.org/F4320333858","display_name":"Michigan Institute for Data Science, University of Michigan","ror":null},{"id":"https://openalex.org/F4320337338","display_name":"National Heart, Lung, and Blood Institute","ror":"https://ror.org/012pb6c26"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4289258409.pdf","grobid_xml":"https://content.openalex.org/works/W4289258409.grobid-xml"},"referenced_works_count":12,"referenced_works":["https://openalex.org/W107263889","https://openalex.org/W2014928429","https://openalex.org/W2093172018","https://openalex.org/W2093639973","https://openalex.org/W2129995532","https://openalex.org/W2131587651","https://openalex.org/W2162557622","https://openalex.org/W2474805097","https://openalex.org/W2523526638","https://openalex.org/W2560099312","https://openalex.org/W2587586954","https://openalex.org/W2997591727"],"related_works":["https://openalex.org/W2383111961","https://openalex.org/W2365952365","https://openalex.org/W2352448290","https://openalex.org/W2380820513","https://openalex.org/W2913146933","https://openalex.org/W2372385138","https://openalex.org/W3191569364","https://openalex.org/W4284898377","https://openalex.org/W155406958","https://openalex.org/W4387088901"],"abstract_inverted_index":{"In":[0,92],"many":[1],"forecasting":[2,61,104,220],"applications,":[3,39],"it":[4],"is":[5,34],"valuable":[6],"to":[7,30,66,76,84,126,167,187],"predict":[8,67],"not":[9],"only":[10],"the":[11,22,26,41,45,52,71,89,110,116,127,154,162,192,204,207],"value":[12],"of":[13,44,94,112,115,133,140,147,156,209],"a":[14,17,60,119,138,143,188],"signal":[15,117,164],"at":[16],"certain":[18],"time":[19],"point":[20],"in":[21,37,70,105,160,213],"future,":[23],"but":[24],"also":[25],"values":[27,69,114],"leading":[28,186],"up":[29],"that":[31,175],"point.":[32],"This":[33,56],"especially":[35],"true":[36],"clinical":[38],"where":[40,63],"future":[42,72,113],"state":[43],"patient":[46],"can":[47,79],"be":[48],"less":[49],"important":[50],"than":[51],"patient's":[53],"overall":[54],"trajectory.":[55],"requires":[57],"multi-step":[58,103,219],"forecasting,":[59],"variant":[62],"one":[64],"aims":[65],"multiple":[68],"simultaneously.":[73],"Standard":[74],"methods":[75],"accomplish":[77],"this":[78],"propagate":[80],"error":[81,201],"from":[82],"prediction":[83,120],"prediction,":[85],"reducing":[86],"quality":[87],"over":[88,118,191],"long":[90],"term.":[91],"light":[93],"these":[95,124],"challenges,":[96],"we":[97,107,152,173],"propose":[98],"multi-output":[99],"deep":[100,171],"architectures":[101],"for":[102],"which":[106],"explicitly":[108],"model":[109],"distribution":[111],"horizon.":[121],"We":[122],"apply":[123],"techniques":[125],"challenging":[128],"and":[129,170,182,218],"clinically":[130],"relevant":[131],"task":[132],"blood":[134,149,215],"glucose":[135,150,216],"forecasting.":[136],"Through":[137],"series":[139],"experiments":[141],"on":[142],"real-world":[144],"dataset":[145],"consisting":[146],"550K":[148],"measurements,":[151],"demonstrate":[153],"effectiveness":[155],"our":[157,176,210],"proposed":[158,177,211],"approaches":[159,178],"capturing":[161],"underlying":[163],"dynamics.":[165],"Compared":[166],"existing":[168],"shallow":[169],"methods,":[172],"find":[174],"improve":[179],"performance":[180],"individually":[181],"capture":[183],"complementary":[184],"information,":[185],"large":[189],"improvement":[190],"baseline":[193],"when":[194],"combined":[195],"(4.87":[196],"vs.":[197],"5.31":[198],"absolute":[199],"percentage":[200],"(APE)).":[202],"Overall,":[203],"results":[205],"suggest":[206],"efficacy":[208],"approach":[212],"predicting":[214],"level":[217],"more":[221],"generally.":[222]},"counts_by_year":[{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":13},{"year":2023,"cited_by_count":7},{"year":2022,"cited_by_count":7},{"year":2021,"cited_by_count":12},{"year":2020,"cited_by_count":10},{"year":2019,"cited_by_count":2}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2022-08-01T00:00:00"}