{"id":"https://openalex.org/W7134237590","doi":"https://doi.org/10.48550/arxiv.2603.05997","title":"MM-ISTS: Cooperating Irregularly Sampled Time Series Forecasting with Multimodal Vision-Text LLMs","display_name":"MM-ISTS: Cooperating Irregularly Sampled Time Series Forecasting with Multimodal Vision-Text LLMs","publication_year":2026,"publication_date":"2026-03-06","ids":{"openalex":"https://openalex.org/W7134237590","doi":"https://doi.org/10.48550/arxiv.2603.05997"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.05997","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.05997","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":null,"license_id":null,"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.2603.05997","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5088098617","display_name":"Zhi Lei","orcid":"https://orcid.org/0000-0003-0169-7666"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lei, Zhi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128597275","display_name":"Chenxi Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Chenxi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128421203","display_name":"Hao Miao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Miao, Hao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5099247444","display_name":"Wanghui Qiu","orcid":"https://orcid.org/0009-0006-1896-6913"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qiu, Wanghui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128610814","display_name":"Bin Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Bin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5128504789","display_name":"Chenjuan Guo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guo, Chenjuan","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":0.3671000003814697,"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":0.3671000003814697,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.1363999992609024,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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":0.1014999970793724,"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/asynchronous-communication","display_name":"Asynchronous communication","score":0.6200000047683716},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.6180999875068665},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.5577999949455261},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5544000267982483},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.4810999929904938},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.46970000863075256},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.45329999923706055},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.4278999865055084},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.37380000948905945}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7705000042915344},{"id":"https://openalex.org/C151319957","wikidata":"https://www.wikidata.org/wiki/Q752739","display_name":"Asynchronous communication","level":2,"score":0.6200000047683716},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.6180999875068665},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.588100016117096},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.5577999949455261},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5544000267982483},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.4810999929904938},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.46970000863075256},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.45329999923706055},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.4278999865055084},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39089998602867126},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.37380000948905945},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.36340001225471497},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.33739998936653137},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3230000138282776},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.3181000053882599},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.3027999997138977},{"id":"https://openalex.org/C2780226545","wikidata":"https://www.wikidata.org/wiki/Q6888030","display_name":"Modality (human\u2013computer interaction)","level":2,"score":0.29190000891685486},{"id":"https://openalex.org/C68859911","wikidata":"https://www.wikidata.org/wiki/Q1503724","display_name":"Pattern matching","level":2,"score":0.2815999984741211},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.27799999713897705},{"id":"https://openalex.org/C88516994","wikidata":"https://www.wikidata.org/wiki/Q1268863","display_name":"Dynamic time warping","level":2,"score":0.2766000032424927},{"id":"https://openalex.org/C182407805","wikidata":"https://www.wikidata.org/wiki/Q2626534","display_name":"Substring","level":3,"score":0.2736000120639801},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.27239999175071716},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2653000056743622},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.2615000009536743},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2556999921798706}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.05997","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.05997","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.05997","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.05997","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"score":0.44308024644851685,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Irregularly":[0],"sampled":[1],"time":[2,15],"series":[3],"(ISTS)":[4],"are":[5],"widespread":[6],"in":[7,36,104,150],"real-world":[8],"scenarios,":[9],"exhibiting":[10],"asynchronous":[11],"observations":[12,28],"on":[13,173],"uneven":[14],"intervals":[16],"across":[17],"diverse":[18],"variables.":[19],"Existing":[20],"ISTS":[21,53,112,122],"forecasting":[22,54],"methods":[23],"often":[24],"solely":[25],"utilize":[26],"historical":[27,121],"to":[29,84,140,166],"predict":[30],"future":[31],"ones":[32],"while":[33,145],"falling":[34],"short":[35],"learning":[37],"contextual":[38,102],"semantics":[39],"and":[40,66,90,100,128],"fine-grained":[41],"temporal":[42,98,118],"patterns.":[43],"To":[44],"address":[45],"these":[46],"problems,":[47],"we":[48,133],"propose":[49,134],"MM-ISTS,":[50],"a":[51,71,77,129,157],"multimodal":[52,107],"framework":[55],"augmented":[56],"by":[57],"vision-text":[58],"large":[59],"language":[60],"models,":[61],"which":[62,149],"bridges":[63],"temporal,":[64],"visual,":[65],"textual":[67,91],"modalities.":[68],"MM-ISTS":[69],"encompasses":[70],"two-stage":[72],"encoding":[73,113],"mechanism.":[74],"In":[75,110,155],"particular,":[76],"Cross-Modal":[78],"Vision-Text":[79],"Encoding":[80],"module":[81,160],"is":[82,164],"proposed":[83,183],"automatically":[85],"generate":[86],"informative":[87],"visual":[88],"images":[89],"data,":[92],"enabling":[93],"the":[94,168,179,182],"capture":[95],"of":[96,181],"intricate":[97],"patterns":[99],"comprehensive":[101],"understanding,":[103],"collaboration":[105],"with":[106,161],"LLMs":[108],"(MLLMs).":[109],"parallel,":[111],"extracts":[114],"complementary":[115],"yet":[116],"enriched":[117],"features":[119],"from":[120],"observations,":[123],"including":[124],"multi-view":[125],"embedding":[126],"fusion":[127],"Temporal-Variable":[130],"Encoder.":[131],"Further,":[132],"an":[135],"Adaptive":[136],"Query-Based":[137],"Feature":[138],"Extractor":[139],"compress":[141],"MLLM":[142],"token":[143],"embeddings":[144],"preserving":[146],"useful":[147],"knowledge,":[148],"turn":[151],"reduces":[152],"computational":[153],"costs.":[154],"addition,":[156],"Multimodal":[158],"Alignment":[159],"Modality-Aware":[162],"Gating":[163],"designed":[165],"alleviate":[167],"modality":[169],"gaps.":[170],"Extensive":[171],"experiments":[172],"real":[174],"data":[175],"offer":[176],"insight":[177],"into":[178],"effectiveness":[180],"solutions.":[184]},"counts_by_year":[],"updated_date":"2026-08-16T07:02:28.622633","created_date":"2026-03-10T00:00:00"}