{"id":"https://openalex.org/W4394662389","doi":"https://doi.org/10.48550/arxiv.2404.04885","title":"TimeGPT in Load Forecasting: A Large Time Series Model Perspective","display_name":"TimeGPT in Load Forecasting: A Large Time Series Model Perspective","publication_year":2024,"publication_date":"2024-04-07","ids":{"openalex":"https://openalex.org/W4394662389","doi":"https://doi.org/10.48550/arxiv.2404.04885"},"language":"en","primary_location":{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2404.04885","pdf_url":"https://arxiv.org/pdf/2404.04885","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":["Cornell University"],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false},"type":"preprint","type_crossref":"posted-content","indexed_in":["arxiv"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2404.04885","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5102868046","display_name":"Wenlong Liao","orcid":"https://orcid.org/0000-0003-1175-0280"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liao, Wenlong","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089732960","display_name":"Fernando Port\u00e9\u2010Agel","orcid":"https://orcid.org/0000-0002-9913-3350"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Porte-Agel, Fernando","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074029890","display_name":"Jiannong Fang","orcid":"https://orcid.org/0000-0002-9205-6444"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fang, Jiannong","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028841407","display_name":"Christian Rehtanz","orcid":"https://orcid.org/0000-0002-8134-6841"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rehtanz, Christian","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059741928","display_name":"Shouxiang Wang","orcid":"https://orcid.org/0000-0002-3892-3703"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Shouxiang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101652087","display_name":"Dechang Yang","orcid":"https://orcid.org/0000-0002-0971-6705"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Dechang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5101564203","display_name":"Zhe Yang","orcid":"https://orcid.org/0000-0002-9421-7029"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Zhe","raw_affiliation_strings":[],"affiliations":[]}],"institution_assertions":[],"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":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":0,"max":84},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9637,"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":0.9637,"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/T10320","display_name":"Neural Networks and Applications","score":0.9488,"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/T13717","display_name":"Advanced Algorithms and Applications","score":0.9201,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[],"concepts":[{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.71312267},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.70745975},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4300057},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.42914852},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.42625722},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.28577548},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.1856477},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.10153365},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.071144104},{"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":[{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2404.04885","pdf_url":"https://arxiv.org/pdf/2404.04885","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":["Cornell University"],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false}],"best_oa_location":{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2404.04885","pdf_url":"https://arxiv.org/pdf/2404.04885","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":["Cornell University"],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false},"sustainable_development_goals":[],"grants":[],"datasets":[],"versions":[],"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2622688551","https://openalex.org/W2390279801","https://openalex.org/W2376932109","https://openalex.org/W2358668433","https://openalex.org/W2149537132","https://openalex.org/W2018871932","https://openalex.org/W1990205660","https://openalex.org/W1550175370"],"abstract_inverted_index":{"Machine":[0],"learning":[1,140],"models":[2,32,52,141],"have":[3],"made":[4],"significant":[5],"progress":[6],"in":[7,16,34,53],"load":[8,20,54,106,127,146,174,193],"forecasting,":[9],"but":[10],"their":[11],"forecast":[12],"accuracy":[13],"is":[14,22,66,77,108,168,227],"limited":[15],"cases":[17],"where":[18],"historical":[19,58,105,206],"data":[21,90,107,121,194,207],"scarce.":[23],"Inspired":[24],"by":[25,187],"the":[26,46,61,103,112,120,135,180,188,192,196,205,219,228],"outstanding":[27],"performance":[28,181],"of":[29,48,87,182],"large":[30,49,62],"language":[31,39],"(LLMs)":[33],"computer":[35],"vision":[36],"and":[37,81,123,142,195,212,216],"natural":[38],"processing,":[40],"this":[41],"paper":[42],"aims":[43],"to":[44,110,117,119,171,223],"discuss":[45],"potential":[47],"time":[50,63,70,83],"series":[51,64,71,84],"forecasting":[55,147,175],"with":[56,126,152,176],"scarce":[57,104,153,177],"data.":[59,198],"Specifically,":[60],"model":[65],"constructed":[67],"as":[68],"a":[69,209,213,232],"generative":[72],"pre-trained":[73],"transformer":[74],"(TimeGPT),":[75],"which":[76,114],"trained":[78],"on":[79,148],"massive":[80],"diverse":[82],"datasets":[85,151],"consisting":[86],"100":[88],"billion":[89],"points":[91],"(e.g.,":[92,137],"finance,":[93],"transportation,":[94],"banking,":[95],"web":[96],"traffic,":[97],"weather,":[98],"energy,":[99],"healthcare,":[100],"etc.).":[101],"Then,":[102],"used":[109],"fine-tune":[111],"TimeGPT,":[113],"helps":[115],"it":[116,162],"adapt":[118],"distribution":[122,189],"characteristics":[124],"associated":[125],"forecasting.":[128],"Simulation":[129],"results":[130],"show":[131],"that":[132,166],"TimeGPT":[133,167,183,226],"outperforms":[134],"benchmarks":[136,172],"popular":[138],"machine":[139],"statistical":[143],"models)":[144],"for":[145,157,173,231],"several":[149],"real":[150],"training":[154,197,210],"samples,":[155],"particularly":[156],"short":[158],"look-ahead":[159],"times.":[160],"However,":[161],"cannot":[163],"be":[164,185],"guaranteed":[165],"always":[169],"superior":[170],"data,":[178],"since":[179],"may":[184],"affected":[186],"differences":[190],"between":[191],"In":[199],"practical":[200],"applications,":[201],"we":[202],"can":[203],"divide":[204],"into":[208],"set":[211,221],"validation":[214,220],"set,":[215],"then":[217],"use":[218],"loss":[222],"decide":[224],"whether":[225],"best":[229],"choice":[230],"specific":[233],"dataset.":[234]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W4394662389","counts_by_year":[],"updated_date":"2024-12-10T16:10:20.232118","created_date":"2024-04-11"}