{"id":"https://openalex.org/W4281251945","doi":"https://doi.org/10.1145/3514094.3534183","title":"Practical Skills Demand Forecasting via Representation Learning of Temporal Dynamics","display_name":"Practical Skills Demand Forecasting via Representation Learning of Temporal Dynamics","publication_year":2022,"publication_date":"2022-07-26","ids":{"openalex":"https://openalex.org/W4281251945","doi":"https://doi.org/10.1145/3514094.3534183"},"language":"en","primary_location":{"id":"doi:10.1145/3514094.3534183","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3514094.3534183","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2205.09508","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5091747186","display_name":"Maysa M. G. Macedo","orcid":"https://orcid.org/0000-0002-9365-9378"},"institutions":[{"id":"https://openalex.org/I4210113516","display_name":"IBM Research - Brazil","ror":"https://ror.org/01fxqdx25","country_code":"BR","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210113516","https://openalex.org/I4210114115"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Maysa Malfiza Garcia de Macedo","raw_affiliation_strings":["IBM Research, Sao Paulo, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM Research, Sao Paulo, Brazil","institution_ids":["https://openalex.org/I4210113516"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051425256","display_name":"Wyatt Clarke","orcid":null},"institutions":[{"id":"https://openalex.org/I1341412227","display_name":"IBM (United States)","ror":"https://ror.org/05hh8d621","country_code":"US","type":"company","lineage":["https://openalex.org/I1341412227"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wyatt Clarke","raw_affiliation_strings":["IBM Research, Yorktown Heights, NY, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM Research, Yorktown Heights, NY, USA","institution_ids":["https://openalex.org/I1341412227"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057650963","display_name":"Eli Lucherini","orcid":null},"institutions":[{"id":"https://openalex.org/I20089843","display_name":"Princeton University","ror":"https://ror.org/00hx57361","country_code":"US","type":"education","lineage":["https://openalex.org/I20089843"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Eli Lucherini","raw_affiliation_strings":["Princeton University, Princeton, NJ, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Princeton University, Princeton, NJ, USA","institution_ids":["https://openalex.org/I20089843"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110229109","display_name":"Tyler Baldwin","orcid":null},"institutions":[{"id":"https://openalex.org/I1341412227","display_name":"IBM (United States)","ror":"https://ror.org/05hh8d621","country_code":"US","type":"company","lineage":["https://openalex.org/I1341412227"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tyler Baldwin","raw_affiliation_strings":["IBM Research, San Jose, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM Research, San Jose, CA, USA","institution_ids":["https://openalex.org/I1341412227"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108811663","display_name":"Dilermando Queiroz Neto","orcid":null},"institutions":[{"id":"https://openalex.org/I4210113516","display_name":"IBM Research - Brazil","ror":"https://ror.org/01fxqdx25","country_code":"BR","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210113516","https://openalex.org/I4210114115"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Dilermando Queiroz Neto","raw_affiliation_strings":["IBM Research, Sao Paulo, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM Research, Sao Paulo, Brazil","institution_ids":["https://openalex.org/I4210113516"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046707914","display_name":"Rog\u00e9rio Abreu de Paula","orcid":null},"institutions":[{"id":"https://openalex.org/I4210113516","display_name":"IBM Research - Brazil","ror":"https://ror.org/01fxqdx25","country_code":"BR","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210113516","https://openalex.org/I4210114115"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Rogerio Abreu de Paula","raw_affiliation_strings":["IBM Research, Sao Paulo, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM Research, Sao Paulo, Brazil","institution_ids":["https://openalex.org/I4210113516"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5011183160","display_name":"Subhro Das","orcid":"https://orcid.org/0000-0002-7610-2738"},"institutions":[{"id":"https://openalex.org/I1341412227","display_name":"IBM (United States)","ror":"https://ror.org/05hh8d621","country_code":"US","type":"company","lineage":["https://openalex.org/I1341412227"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Subhro Das","raw_affiliation_strings":["MIT-IBM Watson AI Lab, IBM Research, Cambridge, MA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"MIT-IBM Watson AI Lab, IBM Research, Cambridge, MA, USA","institution_ids":["https://openalex.org/I1341412227"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"285","last_page":"294"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11122","display_name":"Online Learning and Analytics","score":0.9771999716758728,"subfield":{"id":"https://openalex.org/subfields/1706","display_name":"Computer Science Applications"},"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/T11122","display_name":"Online Learning and Analytics","score":0.9771999716758728,"subfield":{"id":"https://openalex.org/subfields/1706","display_name":"Computer Science Applications"},"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/T12659","display_name":"Innovation Diffusion and Forecasting","score":0.9620000123977661,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10462","display_name":"Reinforcement Learning in Robotics","score":0.9613000154495239,"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/univariate","display_name":"Univariate","score":0.6102263927459717},{"id":"https://openalex.org/keywords/supply-and-demand","display_name":"Supply and demand","score":0.5985569357872009},{"id":"https://openalex.org/keywords/workforce","display_name":"Workforce","score":0.5365339517593384},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4651333689689636},{"id":"https://openalex.org/keywords/work","display_name":"Work (physics)","score":0.4459121823310852},{"id":"https://openalex.org/keywords/moment","display_name":"Moment (physics)","score":0.424490749835968},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.34675103425979614},{"id":"https://openalex.org/keywords/labour-economics","display_name":"Labour economics","score":0.34144675731658936},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.33768904209136963},{"id":"https://openalex.org/keywords/microeconomics","display_name":"Microeconomics","score":0.24727919697761536},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1489238142967224},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.12053188681602478}],"concepts":[{"id":"https://openalex.org/C199163554","wikidata":"https://www.wikidata.org/wiki/Q1681619","display_name":"Univariate","level":3,"score":0.6102263927459717},{"id":"https://openalex.org/C120330832","wikidata":"https://www.wikidata.org/wiki/Q166656","display_name":"Supply and demand","level":2,"score":0.5985569357872009},{"id":"https://openalex.org/C2778139618","wikidata":"https://www.wikidata.org/wiki/Q13440398","display_name":"Workforce","level":2,"score":0.5365339517593384},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4651333689689636},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.4459121823310852},{"id":"https://openalex.org/C179254644","wikidata":"https://www.wikidata.org/wiki/Q13222844","display_name":"Moment (physics)","level":2,"score":0.424490749835968},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.34675103425979614},{"id":"https://openalex.org/C145236788","wikidata":"https://www.wikidata.org/wiki/Q28161","display_name":"Labour economics","level":1,"score":0.34144675731658936},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.33768904209136963},{"id":"https://openalex.org/C175444787","wikidata":"https://www.wikidata.org/wiki/Q39072","display_name":"Microeconomics","level":1,"score":0.24727919697761536},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1489238142967224},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.12053188681602478},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C74650414","wikidata":"https://www.wikidata.org/wiki/Q11397","display_name":"Classical mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C50522688","wikidata":"https://www.wikidata.org/wiki/Q189833","display_name":"Economic growth","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}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1145/3514094.3534183","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3514094.3534183","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2205.09508","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2205.09508","pdf_url":"https://arxiv.org/pdf/2205.09508","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"},{"id":"pmh:oai:RePEc:arx:papers:2205.09508","is_oa":false,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4306401271","display_name":"RePEc: Research Papers in Economics","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I77793887","host_organization_name":"Federal Reserve Bank of St. Louis","host_organization_lineage":["https://openalex.org/I77793887"],"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":"preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2205.09508","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2205.09508","pdf_url":"https://arxiv.org/pdf/2205.09508","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"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/8","score":0.5699999928474426,"display_name":"Decent work and economic growth"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W1821977771","https://openalex.org/W1920367837","https://openalex.org/W2064675550","https://openalex.org/W2124405421","https://openalex.org/W2139807141","https://openalex.org/W2157331557","https://openalex.org/W2228966521","https://openalex.org/W2342352817","https://openalex.org/W2508429489","https://openalex.org/W2526781987","https://openalex.org/W2613303232","https://openalex.org/W2624385633","https://openalex.org/W2772386221","https://openalex.org/W2892380133","https://openalex.org/W2909877301","https://openalex.org/W2922903482","https://openalex.org/W2955994533","https://openalex.org/W3005448318","https://openalex.org/W3123388550","https://openalex.org/W3184230151","https://openalex.org/W4206706211","https://openalex.org/W4235096001"],"related_works":["https://openalex.org/W1828158523","https://openalex.org/W2049578243","https://openalex.org/W2000145235","https://openalex.org/W2122079181","https://openalex.org/W1985848810","https://openalex.org/W2889939530","https://openalex.org/W3121881699","https://openalex.org/W2748838164","https://openalex.org/W2066015000","https://openalex.org/W2912721996"],"abstract_inverted_index":{"Rapid":[0],"technological":[1],"innovation":[2],"threatens":[3],"to":[4,27,69,80,106],"leave":[5],"much":[6],"of":[7,37,55,57,77,102,110,135,144,153,174,179],"the":[8,52,61,71,75,100,108,130,151,185],"global":[9],"workforce":[10],"behind.":[11],"Today's":[12],"economy":[13],"juxtaposes":[14],"white-hot":[15],"demand":[16,112,138,175],"for":[17,24,41,176],"skilled":[18],"labor":[19,89],"against":[20],"stagnant":[21],"employment":[22],"prospects":[23],"workers":[25,183],"unprepared":[26],"participate":[28],"in":[29,47,82,184],"a":[30,35,122,133,142,154,158,177],"digital":[31],"economy.":[32],"It":[33],"is":[34,92],"moment":[36],"peril":[38],"and":[39,51,64,86,171],"opportunity":[40],"every":[42],"country,":[43],"with":[44],"outcomes":[45],"measured":[46],"long-term":[48],"capital":[49],"allocation":[50],"life":[53],"satisfaction":[54],"billions":[56],"workers.":[58],"To":[59],"meet":[60],"moment,":[62],"governments":[63],"markets":[65],"must":[66],"find":[67],"ways":[68],"quicken":[70],"rate":[72],"at":[73],"which":[74,124],"supply":[76],"skills":[78,165,180],"reacts":[79],"changes":[81],"demand.":[83],"More":[84],"fully":[85],"quickly":[87],"understanding":[88],"market":[90],"intelligence":[91],"one":[93],"route.":[94],"In":[95],"this":[96],"work,":[97],"we":[98],"explore":[99],"utility":[101],"time":[103],"series":[104],"forecasts":[105,128],"enhance":[107],"value":[109],"skill":[111,137],"data":[113],"gathered":[114],"from":[115],"online":[116],"job":[117],"advertisements.":[118],"This":[119],"paper":[120],"presents":[121],"pipeline":[123],"makes":[125],"one-shot":[126],"multi-step":[127],"into":[129],"future":[131],"using":[132],"decade":[134],"monthly":[136],"observations":[139],"based":[140],"on":[141],"set":[143],"recurrent":[145],"neural":[146],"network":[147],"methods.":[148],"We":[149],"compare":[150],"performance":[152],"multivariate":[155,168],"model":[156,169],"versus":[157],"univariate":[159],"one,":[160],"analyze":[161],"how":[162],"correlation":[163],"between":[164],"can":[166],"influence":[167],"results,":[170],"present":[172],"predictions":[173],"selection":[178],"practiced":[181],"by":[182],"information":[186],"technology":[187],"industry.":[188]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
