{"id":"https://openalex.org/W6929339258","doi":"https://doi.org/10.48550/arxiv.2506.21611","title":"When Does Multimodality Lead to Better Time Series Forecasting?","display_name":"When Does Multimodality Lead to Better Time Series Forecasting?","publication_year":2025,"publication_date":"2025-06-20","ids":{"openalex":"https://openalex.org/W6929339258","doi":"https://doi.org/10.48550/arxiv.2506.21611"},"language":"en","primary_location":{"id":"doi:10.48550/arxiv.2506.21611","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2506.21611","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.2506.21611","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Zhang, Xiyuan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Xiyuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Han, Boran","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Han, Boran","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Fang, Haoyang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fang, Haoyang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Ansari, Abdul Fatir","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ansari, Abdul Fatir","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Zhang, Shuai","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Shuai","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Maddix, Danielle C.","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Maddix, Danielle C.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Hu, Cuixiong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hu, Cuixiong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Wilson, Andrew Gordon","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wilson, Andrew Gordon","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Mahoney, Michael W.","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mahoney, Michael W.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Wang, Hao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Hao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Liu, Yan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Yan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Rangwala, Huzefa","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rangwala, Huzefa","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Karypis, George","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Karypis, George","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Wang, Bernie","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Bernie","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":true,"primary_topic":{"id":"https://openalex.org/T11918","display_name":"Forecasting Techniques and Applications","score":0.2702000141143799,"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":0.2702000141143799,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.1597999930381775,"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/T10028","display_name":"Topic Modeling","score":0.11959999799728394,"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/multimodality","display_name":"Multimodality","score":0.8438000082969666},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6310999989509583},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.6215000152587891},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5748000144958496},{"id":"https://openalex.org/keywords/foundation","display_name":"Foundation (evidence)","score":0.35690000653266907},{"id":"https://openalex.org/keywords/empirical-research","display_name":"Empirical research","score":0.34529998898506165}],"concepts":[{"id":"https://openalex.org/C2780910867","wikidata":"https://www.wikidata.org/wiki/Q1952416","display_name":"Multimodality","level":2,"score":0.8438000082969666},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6621000170707703},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6310999989509583},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.6215000152587891},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5748000144958496},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5325000286102295},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49959999322891235},{"id":"https://openalex.org/C2780966255","wikidata":"https://www.wikidata.org/wiki/Q5474306","display_name":"Foundation (evidence)","level":2,"score":0.35690000653266907},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.34529998898506165},{"id":"https://openalex.org/C166052673","wikidata":"https://www.wikidata.org/wiki/Q83021","display_name":"Empirical evidence","level":2,"score":0.3124000132083893},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.30469998717308044},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.30300000309944153},{"id":"https://openalex.org/C2777093003","wikidata":"https://www.wikidata.org/wiki/Q6508345","display_name":"Lead (geology)","level":2,"score":0.3005000054836273},{"id":"https://openalex.org/C2781468064","wikidata":"https://www.wikidata.org/wiki/Q1267117","display_name":"Lead time","level":2,"score":0.2919999957084656},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.28940001130104065},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.2856000065803528},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.2782000005245209},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.2603999972343445}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2506.21611","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2506.21611","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.2506.21611","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2506.21611","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":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.42738771438598633}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Recently,":[0],"there":[1],"has":[2],"been":[3],"growing":[4],"interest":[5],"in":[6,96],"incorporating":[7,139],"textual":[8],"information":[9,141],"into":[10],"foundation":[11,201],"models":[12,77],"for":[13,78,202],"time":[14,64,153,192],"series":[15,65,154,193],"forecasting.":[16,79],"However,":[17],"it":[18],"remains":[19],"unclear":[20],"whether":[21],"and":[22,51,66,69,121,156,177,213],"under":[23],"what":[24,186],"conditions":[25],"such":[26],"multimodal":[27,57],"integration":[28],"consistently":[29],"yields":[30],"gains.":[31],"We":[32,53],"systematically":[33],"investigate":[34],"these":[35,99],"questions":[36],"across":[37,104,129],"a":[38,198],"diverse":[39],"benchmark":[40],"of":[41,86,117],"16":[42],"forecasting":[43,58,211],"tasks":[44],"spanning":[45],"7":[46],"domains,":[47],"including":[48],"health,":[49],"environment,":[50],"economics.":[52],"evaluate":[54],"two":[55],"popular":[56],"paradigms:":[59],"aligning-based":[60],"methods,":[61,71],"which":[62,72],"align":[63],"text":[67,140,148,180],"representations;":[68],"prompting-based":[70],"directly":[73],"prompt":[74],"large":[75],"language":[76],"Our":[80,131,195],"findings":[81,132],"reveal":[82],"that":[83,127,134,215],"the":[84,115,136,162,179,191],"benefits":[85,217],"multimodality":[87,205],"are":[88,101,167,218],"highly":[89],"condition-dependent.":[90],"While":[91],"we":[92,113],"confirm":[93],"reported":[94],"gains":[95,166],"some":[97],"settings,":[98],"improvements":[100],"not":[102],"universal":[103,220],"datasets":[105],"or":[106],"models.":[107],"To":[108],"move":[109],"beyond":[110,185],"empirical":[111],"observations,":[112],"disentangle":[114],"effects":[116],"model":[118],"architectural":[119],"properties":[120],"data":[122,163,174],"characteristics,":[123],"drawing":[124],"data-agnostic":[125],"insights":[126],"generalize":[128],"domains.":[130],"highlight":[133],"on":[135],"modeling":[137],"side,":[138,164],"is":[142,175,187],"most":[143],"helpful":[144],"given":[145],"(1)":[146],"high-capacity":[147],"models,":[149,155],"(2)":[150],"comparatively":[151],"weaker":[152],"(3)":[157],"appropriate":[158],"aligning":[159],"strategies.":[160],"On":[161],"performance":[165],"more":[168],"likely":[169],"when":[170,204],"(4)":[171],"sufficient":[172],"training":[173],"available":[176],"(5)":[178],"offers":[181,197],"complementary":[182],"predictive":[183],"signal":[184],"already":[188],"captured":[189],"from":[190],"alone.":[194],"study":[196],"rigorous,":[199],"quantitative":[200],"understanding":[203],"can":[206],"be":[207],"expected":[208],"to":[209],"aid":[210],"tasks,":[212],"reveals":[214],"its":[216],"neither":[219],"nor":[221],"always":[222],"aligned":[223],"with":[224],"intuition.":[225]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2025-10-10T00:00:00"}