{"id":"https://openalex.org/W7154410155","doi":"https://doi.org/10.48550/arxiv.2604.11529","title":"TempusBench: An Evaluation Framework for Time-Series Forecasting","display_name":"TempusBench: An Evaluation Framework for Time-Series Forecasting","publication_year":2026,"publication_date":"2026-04-13","ids":{"openalex":"https://openalex.org/W7154410155","doi":"https://doi.org/10.48550/arxiv.2604.11529"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.11529","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.11529","pdf_url":null,"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":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.11529","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5011740775","display_name":"Denizalp Goktas","orcid":"https://orcid.org/0000-0003-1958-685X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Goktas, Denizalp","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021906791","display_name":"Gerardo Ria\u00f1o\u2010Brice\u00f1o","orcid":"https://orcid.org/0000-0002-2995-1139"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ria\u00f1o-Brice\u00f1o, Gerardo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133558359","display_name":"Alif Abdullah","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Abdullah, Alif","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133584021","display_name":"Aryan Nair","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nair, Aryan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133623780","display_name":"Chenkai Shen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shen, Chenkai","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133616995","display_name":"Beatriz de Lucio","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"de Lucio, Beatriz","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006194412","display_name":"Alexandra Magnusson","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Magnusson, Alexandra","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133602572","display_name":"Farhan Mashrur","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mashrur, Farhan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133563044","display_name":"Ahmed Abdulla","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Abdulla, Ahmed","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133584500","display_name":"Shawrna Sen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sen, Shawrna","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133611256","display_name":"Mahitha Thippireddy","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Thippireddy, Mahitha","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133580450","display_name":"Gregory Schwartz","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Schwartz, Gregory","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5088176162","display_name":"Amy Greenwald","orcid":"https://orcid.org/0000-0003-3160-7676"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Greenwald, Amy","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":13,"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/T11918","display_name":"Forecasting Techniques and Applications","score":0.4375,"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"}},"topics":[{"id":"https://openalex.org/T11918","display_name":"Forecasting Techniques and Applications","score":0.4375,"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":0.1005999967455864,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.05130000039935112,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.7171000242233276},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.574400007724762},{"id":"https://openalex.org/keywords/suite","display_name":"Suite","score":0.5583000183105469},{"id":"https://openalex.org/keywords/replicate","display_name":"Replicate","score":0.5081999897956848},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.48330000042915344},{"id":"https://openalex.org/keywords/hyperparameter","display_name":"Hyperparameter","score":0.46000000834465027},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.45239999890327454},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.4424999952316284},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.41339999437332153}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7771999835968018},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.7171000242233276},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.574400007724762},{"id":"https://openalex.org/C79581498","wikidata":"https://www.wikidata.org/wiki/Q1367530","display_name":"Suite","level":2,"score":0.5583000183105469},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5444999933242798},{"id":"https://openalex.org/C2781162219","wikidata":"https://www.wikidata.org/wiki/Q26250693","display_name":"Replicate","level":2,"score":0.5081999897956848},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.48330000042915344},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4641000032424927},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.46000000834465027},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.45239999890327454},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.4424999952316284},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.41339999437332153},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.39629998803138733},{"id":"https://openalex.org/C12590798","wikidata":"https://www.wikidata.org/wiki/Q3933199","display_name":"Replication (statistics)","level":2,"score":0.3880999982357025},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3783999979496002},{"id":"https://openalex.org/C93518851","wikidata":"https://www.wikidata.org/wiki/Q180160","display_name":"Metadata","level":2,"score":0.3774999976158142},{"id":"https://openalex.org/C2164484","wikidata":"https://www.wikidata.org/wiki/Q5170150","display_name":"Core (optical fiber)","level":2,"score":0.3650999963283539},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.3456000089645386},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.33899998664855957},{"id":"https://openalex.org/C168065819","wikidata":"https://www.wikidata.org/wiki/Q845566","display_name":"Debugging","level":2,"score":0.32499998807907104},{"id":"https://openalex.org/C2780366209","wikidata":"https://www.wikidata.org/wiki/Q5170200","display_name":"Core model","level":2,"score":0.323199987411499},{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.320499986410141},{"id":"https://openalex.org/C105002631","wikidata":"https://www.wikidata.org/wiki/Q4833645","display_name":"Subject-matter expert","level":3,"score":0.2865000069141388},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.2736000120639801},{"id":"https://openalex.org/C10485038","wikidata":"https://www.wikidata.org/wiki/Q48996162","display_name":"Hyperparameter optimization","level":3,"score":0.267300009727478},{"id":"https://openalex.org/C2781002164","wikidata":"https://www.wikidata.org/wiki/Q6822311","display_name":"Meta learning (computer science)","level":3,"score":0.26589998602867126},{"id":"https://openalex.org/C172367668","wikidata":"https://www.wikidata.org/wiki/Q6504956","display_name":"Data visualization","level":3,"score":0.2635999917984009},{"id":"https://openalex.org/C2777002779","wikidata":"https://www.wikidata.org/wiki/Q42478","display_name":"Perl","level":2,"score":0.26339998841285706},{"id":"https://openalex.org/C2777904410","wikidata":"https://www.wikidata.org/wiki/Q7397","display_name":"Software","level":2,"score":0.250900000333786}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.11529","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.11529","pdf_url":null,"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":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.11529","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.11529","pdf_url":null,"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":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.6902064085006714,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Foundation":[0],"models":[1,18,30,96,125],"have":[2],"transformed":[3],"natural":[4],"language":[5],"processing":[6],"and":[7,10,41,83,121,140,208,223],"computer":[8],"vision,":[9],"a":[11,39,60,98,138,186,198,203,210,225],"rapidly":[12],"growing":[13],"literature":[14],"on":[15,55,220],"time-series":[16],"foundation":[17],"(TSFMs)":[19],"seeks":[20],"to":[21,89,217],"replicate":[22],"this":[23],"success":[24],"in":[25,180],"forecasting.":[26],"While":[27],"recent":[28],"open-source":[29,165],"demonstrate":[31],"the":[32,36,56,86],"promise":[33],"of":[34,58,78,102,172,188],"TSFMs,":[35],"field":[37],"lacks":[38],"comprehensive":[40],"community-accepted":[42],"model":[43,199],"evaluation":[44,64,166,200],"framework.":[45,61],"We":[46,214],"see":[47],"at":[48,228],"least":[49],"four":[50],"major":[51],"issues":[52],"impeding":[53],"progress":[54],"development":[57],"such":[59,106,118],"First,":[62],"existing":[63,133,181,195],"frameworks":[65,94,134],"comprise":[66],"benchmark":[67,103,190],"forecasting":[68,104],"tasks":[69,191],"derived":[70],"from":[71],"often":[72,129],"outdated":[73],"datasets":[74,175],"(e.g.,":[75,126],"M3),":[76],"many":[77],"which":[79,176],"lack":[80],"clear":[81],"metadata":[82],"overlap":[84],"with":[85,202],"corpora":[87],"used":[88],"pre-train":[90],"TSFMs.":[91,169],"Second,":[92],"these":[93,159],"evaluate":[95],"along":[97],"narrowly":[99],"defined":[100],"set":[101,187],"tasks,":[105],"as":[107,119,132],"forecast":[108],"horizon":[109],"length":[110],"or":[111],"domain,":[112],"but":[113],"overlook":[114],"core":[115],"statistical":[116],"properties":[117],"non-stationarity":[120],"seasonality.":[122],"Third,":[123],"domain-specific":[124],"XGBoost)":[127],"are":[128,155,177],"compared":[130],"unfairly,":[131],"do":[135],"not":[136,178],"enforce":[137],"systematic":[139],"consistent":[141],"hyperparameter":[142,205],"tuning":[143,206],"convention":[144],"for":[145,151,168],"all":[146],"models.":[147],"Fourth,":[148],"visualization":[149,212],"tools":[150],"interpreting":[152],"comparative":[153],"performance":[154],"lacking.":[156],"To":[157],"address":[158],"issues,":[160],"we":[161],"introduce":[162],"TempusBench,":[163],"an":[164],"framework":[167],"TempusBench":[170],"consists":[171],"1)":[173],"new":[174],"included":[179],"TSFM":[182],"pretraining":[183],"corpora,":[184],"2)":[185],"novel":[189],"that":[192],"go":[193],"beyond":[194],"ones,":[196],"3)":[197],"pipeline":[201],"standardized":[204],"protocol,":[207],"4)":[209],"tensorboard-based":[211],"interface.":[213],"provide":[215],"access":[216],"our":[218],"code":[219],"GitHub:":[221],"https://github.com/Smlcrm/TempusBench":[222],"maintain":[224],"live":[226],"leaderboard":[227],"https://benchmark.smlcrm.com/.":[229]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-04-15T00:00:00"}
