{"id":"https://openalex.org/W4318148243","doi":"https://doi.org/10.1109/bigdata55660.2022.10020927","title":"GQFormer: A Multi-Quantile Generative Transformer for Time Series Forecasting","display_name":"GQFormer: A Multi-Quantile Generative Transformer for Time Series Forecasting","publication_year":2022,"publication_date":"2022-12-17","ids":{"openalex":"https://openalex.org/W4318148243","doi":"https://doi.org/10.1109/bigdata55660.2022.10020927"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata55660.2022.10020927","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata55660.2022.10020927","pdf_url":null,"source":{"id":"https://openalex.org/S4363607709","display_name":"2022 IEEE International Conference on Big Data (Big Data)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5073858605","display_name":"Shayan Jawed","orcid":"https://orcid.org/0009-0001-9130-8208"},"institutions":[{"id":"https://openalex.org/I155765044","display_name":"University of Hildesheim","ror":"https://ror.org/02f9det96","country_code":"DE","type":"education","lineage":["https://openalex.org/I155765044"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Shayan Jawed","raw_affiliation_strings":["University of Hildesheim,Information Systems and Machine Learning Lab,Hildesheim,Germany","Information Systems and Machine Learning Lab, University of Hildesheim, Hildesheim, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Hildesheim,Information Systems and Machine Learning Lab,Hildesheim,Germany","institution_ids":["https://openalex.org/I155765044"]},{"raw_affiliation_string":"Information Systems and Machine Learning Lab, University of Hildesheim, Hildesheim, Germany","institution_ids":["https://openalex.org/I155765044"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5039470755","display_name":"Lars Schmidt-Thieme","orcid":"https://orcid.org/0000-0001-5729-6023"},"institutions":[{"id":"https://openalex.org/I155765044","display_name":"University of Hildesheim","ror":"https://ror.org/02f9det96","country_code":"DE","type":"education","lineage":["https://openalex.org/I155765044"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Lars Schmidt-Thieme","raw_affiliation_strings":["University of Hildesheim,Information Systems and Machine Learning Lab,Hildesheim,Germany","Information Systems and Machine Learning Lab, University of Hildesheim, Hildesheim, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Hildesheim,Information Systems and Machine Learning Lab,Hildesheim,Germany","institution_ids":["https://openalex.org/I155765044"]},{"raw_affiliation_string":"Information Systems and Machine Learning Lab, University of Hildesheim, Hildesheim, Germany","institution_ids":["https://openalex.org/I155765044"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I155765044"],"apc_list":null,"apc_paid":null,"fwci":0.5537,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.65245496,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"992","last_page":"1001"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9969000220298767,"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":0.9969000220298767,"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/T11918","display_name":"Forecasting Techniques and Applications","score":0.9879999756813049,"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/T11326","display_name":"Stock Market Forecasting Methods","score":0.9800000190734863,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/quantile","display_name":"Quantile","score":0.9286037087440491},{"id":"https://openalex.org/keywords/quantile-function","display_name":"Quantile function","score":0.6919372081756592},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.617847740650177},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5467802286148071},{"id":"https://openalex.org/keywords/probabilistic-forecasting","display_name":"Probabilistic forecasting","score":0.477020263671875},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.462833434343338},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.39855754375457764},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.34291526675224304},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.25413060188293457},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.22373497486114502},{"id":"https://openalex.org/keywords/probability-density-function","display_name":"Probability density function","score":0.19136309623718262},{"id":"https://openalex.org/keywords/cumulative-distribution-function","display_name":"Cumulative distribution function","score":0.17670515179634094}],"concepts":[{"id":"https://openalex.org/C118671147","wikidata":"https://www.wikidata.org/wiki/Q578714","display_name":"Quantile","level":2,"score":0.9286037087440491},{"id":"https://openalex.org/C47121976","wikidata":"https://www.wikidata.org/wiki/Q3489473","display_name":"Quantile function","level":4,"score":0.6919372081756592},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.617847740650177},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5467802286148071},{"id":"https://openalex.org/C122282355","wikidata":"https://www.wikidata.org/wiki/Q7246855","display_name":"Probabilistic forecasting","level":3,"score":0.477020263671875},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.462833434343338},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.39855754375457764},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.34291526675224304},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.25413060188293457},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.22373497486114502},{"id":"https://openalex.org/C197055811","wikidata":"https://www.wikidata.org/wiki/Q207522","display_name":"Probability density function","level":2,"score":0.19136309623718262},{"id":"https://openalex.org/C103784038","wikidata":"https://www.wikidata.org/wiki/Q386228","display_name":"Cumulative distribution function","level":3,"score":0.17670515179634094},{"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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata55660.2022.10020927","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata55660.2022.10020927","pdf_url":null,"source":{"id":"https://openalex.org/S4363607709","display_name":"2022 IEEE International Conference on Big Data (Big Data)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":51,"referenced_works":["https://openalex.org/W2025720061","https://openalex.org/W2119839985","https://openalex.org/W2552480641","https://openalex.org/W2773625660","https://openalex.org/W2803308811","https://openalex.org/W2889928394","https://openalex.org/W2921829327","https://openalex.org/W2954731415","https://openalex.org/W2962101532","https://openalex.org/W2963374284","https://openalex.org/W2980994438","https://openalex.org/W3005921148","https://openalex.org/W3092866682","https://openalex.org/W3097294131","https://openalex.org/W3097882722","https://openalex.org/W3157506437","https://openalex.org/W3171884590","https://openalex.org/W3177318507","https://openalex.org/W3180769366","https://openalex.org/W3181461345","https://openalex.org/W3206627494","https://openalex.org/W3212890323","https://openalex.org/W3213153375","https://openalex.org/W4200633557","https://openalex.org/W4226406930","https://openalex.org/W4241996101","https://openalex.org/W4285875893","https://openalex.org/W4287365706","https://openalex.org/W4301742181","https://openalex.org/W4385245566","https://openalex.org/W6729542563","https://openalex.org/W6739901393","https://openalex.org/W6746729860","https://openalex.org/W6751629939","https://openalex.org/W6753513663","https://openalex.org/W6754779804","https://openalex.org/W6760513793","https://openalex.org/W6764679822","https://openalex.org/W6766065261","https://openalex.org/W6766188800","https://openalex.org/W6773967291","https://openalex.org/W6783558938","https://openalex.org/W6784835917","https://openalex.org/W6788328477","https://openalex.org/W6795140394","https://openalex.org/W6797155008","https://openalex.org/W6797754615","https://openalex.org/W6802225155","https://openalex.org/W6803833089","https://openalex.org/W6805238626","https://openalex.org/W6811092542"],"related_works":["https://openalex.org/W3136930549","https://openalex.org/W2911373886","https://openalex.org/W2790774582","https://openalex.org/W2909436466","https://openalex.org/W2769304616","https://openalex.org/W2031337030","https://openalex.org/W2561944894","https://openalex.org/W4382774528","https://openalex.org/W2792247961","https://openalex.org/W4246717623"],"abstract_inverted_index":{"We":[0,149],"propose":[1],"GQFormer,":[2],"a":[3,100,116],"probabilistic":[4,159],"time":[5],"series":[6],"forecasting":[7,73,92,160],"method":[8,156],"that":[9,79,104,122],"models":[10,106],"the":[11,15,23,31,43,57,84,134,142,146,152,155,165],"quantile":[12,40,50,72,88,108,125],"function":[13,120],"of":[14,42,145,154],"forecast":[16],"distribution.":[17,45,148],"Our":[18,66],"methodology":[19],"is":[20,68],"rooted":[21],"in":[22,63],"Implicit":[24],"Quantile":[25],"modeling":[26,51,83],"approach,":[27],"where":[28],"samples":[29],"from":[30,70],"Uniform":[32],"distribution":[33,59],"$\\mathcal{U}\\left(":[34],"{0,1}":[35],"\\right)$":[36],"are":[37],"reparameterized":[38],"to":[39,81,127,140,157,164],"values":[41],"target":[44],"This":[46],"allows":[47],"implicit":[48],"generative":[49],"without":[52],"any":[53],"prior":[54,64,71],"assumptions":[55],"on":[56,133],"data":[58],"like":[60],"Gaussianity,":[61],"common":[62],"works.":[65],"work":[67],"distinguished":[69],"methods":[74],"by":[75],"novel":[76,117],"methodological":[77],"advances":[78],"relate":[80],"directly":[82],"correlations":[85],"among":[86],"multiple":[87,107],"estimations":[89,109,126,130],"at":[90],"each":[91],"horizon.":[93],"To":[94],"this":[95],"end,":[96],"we":[97],"firstly":[98],"develop":[99],"parameters":[101],"haring":[102],"architecture":[103],"implicitly":[105],"efficiently":[110],"and":[111,132,162],"secondly":[112],"regularize":[113],"these":[114],"through":[115],"multi-task":[118],"loss":[119,166],"formulation":[121],"optimizes":[123],"for":[124],"be":[128,136],"sharper":[129],"individually":[131],"whole":[135],"spread":[137],"maximally":[138],"apart":[139],"capture":[141],"various":[143],"modes":[144],"underlying":[147],"experimentally":[150],"validate":[151],"superiority":[153],"state-of-the-art":[158],"baselines":[161],"ablations":[163],"formulation.":[167]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}