{"id":"https://openalex.org/W7164188145","doi":"https://doi.org/10.48550/arxiv.2606.10466","title":"UPLOTS: A Unified Pretrained Language Model for Constrained Time-series Generation","display_name":"UPLOTS: A Unified Pretrained Language Model for Constrained Time-series Generation","publication_year":2026,"publication_date":"2026-06-09","ids":{"openalex":"https://openalex.org/W7164188145","doi":"https://doi.org/10.48550/arxiv.2606.10466"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.10466","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.10466","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.10466","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5138318929","display_name":"Du Yin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yin, Du","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138350504","display_name":"Hao Xue","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xue, Hao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000626453","display_name":"Jinliang Deng","orcid":"https://orcid.org/0000-0002-0759-947X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Deng, Jinliang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138340092","display_name":"Yang Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Yang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138369284","display_name":"Shuang Ao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ao, Shuang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066321379","display_name":"Arian Prabowo","orcid":"https://orcid.org/0000-0002-0459-354X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Prabowo, Arian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5138321959","display_name":"Flora D. Salim","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Salim, Flora","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.3287000060081482,"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":0.3287000060081482,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.16279999911785126,"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/T11326","display_name":"Stock Market Forecasting Methods","score":0.10760000348091125,"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/leverage","display_name":"Leverage (statistics)","score":0.7038999795913696},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.6694999933242798},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.6592000126838684},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.4878999888896942},{"id":"https://openalex.org/keywords/constraint","display_name":"Constraint (computer-aided design)","score":0.4618000090122223},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.439300000667572},{"id":"https://openalex.org/keywords/code-generation","display_name":"Code generation","score":0.3723999857902527}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8184000253677368},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.7038999795913696},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.6694999933242798},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.6592000126838684},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5370000004768372},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.4878999888896942},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.4618000090122223},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.439300000667572},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39100000262260437},{"id":"https://openalex.org/C133162039","wikidata":"https://www.wikidata.org/wiki/Q1061077","display_name":"Code generation","level":3,"score":0.3723999857902527},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.365200012922287},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.31709998846054077},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.3127000033855438},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.30090001225471497},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.29919999837875366},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.2897999882698059},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.2635999917984009},{"id":"https://openalex.org/C206729178","wikidata":"https://www.wikidata.org/wiki/Q2271896","display_name":"Scheduling (production processes)","level":2,"score":0.25870001316070557},{"id":"https://openalex.org/C173404611","wikidata":"https://www.wikidata.org/wiki/Q528588","display_name":"Constraint programming","level":3,"score":0.25130000710487366},{"id":"https://openalex.org/C2775907273","wikidata":"https://www.wikidata.org/wiki/Q7805281","display_name":"Time constraint","level":2,"score":0.250900000333786}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.10466","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.10466","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.10466","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.10466","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","score":0.6308978796005249,"id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"In":[0],"time-series":[1],"generation,":[2],"existing":[3],"approaches":[4],"typically":[5],"handcraft":[6],"ortrain":[7],"a":[8,34,54],"separate":[9],"model":[10,38],"for":[11],"each":[12],"dataset,":[13],"which":[14,83],"hinders":[15],"their":[16],"scalability":[17],"and":[18,80,93,106,114,120,134,144],"fails":[19],"to":[20,86],"leverage":[21],"shared":[22],"temporal":[23,89],"structures":[24,90],"across":[25,44],"domains.":[26,46],"To":[27],"address":[28],"this":[29],"fragmentation,":[30],"we":[31],"propose":[32],"UPLOTS,":[33],"Unified,":[35],"Prompt-guided":[36],"Language":[37],"framework":[39],"fOr":[40],"constrained":[41],"Time-Series":[42],"Generation":[43],"diverse":[45,88],"Instead":[47],"of":[48],"building":[49],"task-specific":[50],"models,":[51],"UPLOTS":[52,85,101,127],"leverages":[53],"single":[55],"pre-trained":[56],"transformer":[57],"backbone":[58],"guided":[59],"by":[60],"learned":[61],"constraint":[62,108],"prompts,":[63],"enabling":[64],"on-demand":[65],"generation":[66],"with":[67],"precise":[68],"pattern":[69],"control.":[70],"One":[71],"key":[72],"innovation":[73],"is":[74],"our":[75],"dynamic":[76],"multi-dataset":[77],"loss":[78],"re-weighting":[79],"prompt-to-pattern":[81],"mapping,":[82],"allows":[84],"internalize":[87],"during":[91],"training":[92],"conditionally":[94],"generate":[95],"them":[96],"at":[97,148],"inference.":[98],"We":[99],"evaluate":[100],"on":[102],"four":[103],"real-world":[104],"benchmarks":[105],"multiple":[107],"settings,":[109],"including":[110],"peak-period,":[111],"calendar,":[112],"load-level,":[113],"volatility":[115],"patterns.":[116],"Additional":[117],"held-out":[118],"constraint-combination":[119],"downstream":[121],"forecasting":[122],"experiments":[123],"further":[124],"demonstrate":[125],"that":[126],"generalizes":[128],"beyond":[129],"the":[130],"original":[131],"peak-pattern":[132],"setting":[133],"improves":[135],"data":[136],"augmentation":[137],"under":[138],"scarce":[139],"real-data":[140],"regimes.":[141],"Our":[142],"code":[143],"baselines":[145],"are":[146],"available":[147],"github":[149],"repo:":[150],"https://github.com/cruiseresearchgroup/UPLOTS.":[151]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-11T00:00:00"}
