{"id":"https://openalex.org/W7167265752","doi":"https://doi.org/10.48550/arxiv.2607.01918","title":"Zeus: Towards Tuning-Free Foundation Model for Time Series Analysis","display_name":"Zeus: Towards Tuning-Free Foundation Model for Time Series Analysis","publication_year":2026,"publication_date":"2026-07-02","ids":{"openalex":"https://openalex.org/W7167265752","doi":"https://doi.org/10.48550/arxiv.2607.01918"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2607.01918","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.01918","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.2607.01918","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5125775062","display_name":"Yisong Fu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fu, Yisong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139958023","display_name":"Zezhi Shao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shao, Zezhi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043761995","display_name":"C Shijia Yu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu, Chengqing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5140012468","display_name":"Yujie Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Yujie","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5140012279","display_name":"Yongjun Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Yongjun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139956454","display_name":"Xueqi Cheng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cheng, Xueqi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5029787563","display_name":"Fang Wang","orcid":"https://orcid.org/0000-0002-3327-4177"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Fei","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":0.5029000043869019,"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":0.5029000043869019,"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":0.18930000066757202,"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":0.047200001776218414,"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/granularity","display_name":"Granularity","score":0.7161999940872192},{"id":"https://openalex.org/keywords/zeus","display_name":"ZEUS (particle detector)","score":0.6222000122070312},{"id":"https://openalex.org/keywords/fidelity","display_name":"Fidelity","score":0.4851999878883362},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.4652999937534332},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.4090000092983246},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.38749998807907104},{"id":"https://openalex.org/keywords/high-fidelity","display_name":"High fidelity","score":0.2946000099182129}],"concepts":[{"id":"https://openalex.org/C177774035","wikidata":"https://www.wikidata.org/wiki/Q1246948","display_name":"Granularity","level":2,"score":0.7161999940872192},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.678600013256073},{"id":"https://openalex.org/C2776444479","wikidata":"https://www.wikidata.org/wiki/Q8063038","display_name":"ZEUS (particle detector)","level":5,"score":0.6222000122070312},{"id":"https://openalex.org/C2776459999","wikidata":"https://www.wikidata.org/wiki/Q2119376","display_name":"Fidelity","level":2,"score":0.4851999878883362},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.4652999937534332},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.4090000092983246},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.38749998807907104},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.31439998745918274},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.29649999737739563},{"id":"https://openalex.org/C113364801","wikidata":"https://www.wikidata.org/wiki/Q26674","display_name":"High fidelity","level":2,"score":0.2946000099182129},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.28299999237060547},{"id":"https://openalex.org/C176982825","wikidata":"https://www.wikidata.org/wiki/Q835922","display_name":"Lexical analysis","level":2,"score":0.26759999990463257},{"id":"https://openalex.org/C2777402240","wikidata":"https://www.wikidata.org/wiki/Q6783436","display_name":"Masking (illustration)","level":2,"score":0.2662999927997589},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2637999951839447},{"id":"https://openalex.org/C45493050","wikidata":"https://www.wikidata.org/wiki/Q7884934","display_name":"Unified Model","level":2,"score":0.26350000500679016},{"id":"https://openalex.org/C96146094","wikidata":"https://www.wikidata.org/wiki/Q609057","display_name":"Unification","level":2,"score":0.2574000060558319},{"id":"https://openalex.org/C2780966255","wikidata":"https://www.wikidata.org/wiki/Q5474306","display_name":"Foundation (evidence)","level":2,"score":0.25360000133514404}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2607.01918","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.01918","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.2607.01918","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.01918","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":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"We":[0],"present":[1],"Zeus,":[2],"a":[3,61,68,93,107,130],"unified":[4,94],"tuning-free":[5,124],"Time":[6],"Series":[7],"Foundation":[8],"Model":[9],"(TSFM)":[10],"that":[11,26,96,117],"delivers":[12],"superior":[13],"performance":[14],"across":[15,84,112],"diverse":[16],"analysis":[17],"tasks":[18,99,115],"without":[19],"any":[20],"task-specific":[21,34],"fine-tuning.":[22],"Unlike":[23],"prior":[24],"studies":[25],"primarily":[27],"focus":[28],"on":[29],"zero-shot":[30],"forecasting":[31],"but":[32],"require":[33],"tuning":[35],"for":[36],"other":[37],"tasks,":[38,86],"Zeus":[39,59,87,118],"bridges":[40],"this":[41],"gap":[42],"by":[43],"addressing":[44],"two":[45],"fundamental":[46],"challenges":[47],"in":[48,123],"multi-task":[49],"generalization.":[50],"First,":[51],"to":[52,79],"reconcile":[53],"point-level":[54],"granularity":[55],"with":[56,75],"long-sequence":[57],"scalability,":[58],"incorporates":[60],"multi-scale":[62],"Transformer":[63],"featuring":[64],"point-wise":[65],"tokenization":[66],"and":[67,103],"U-shaped":[69],"hierarchy,":[70],"effectively":[71],"balancing":[72],"fine-grained":[73],"fidelity":[74],"computational":[76],"efficiency.":[77],"Second,":[78],"accommodate":[80],"varying":[81],"inductive":[82],"biases":[83],"different":[85],"introduces":[88],"Multi-Objective":[89],"Temporal":[90],"Masking":[91],"(MOTM),":[92],"strategy":[95],"supports":[97],"heterogeneous":[98],"(e.g.,":[100],"extrapolation,":[101],"interpolation,":[102],"global":[104],"abstraction)":[105],"within":[106],"single":[108],"framework.":[109],"Extensive":[110],"experiments":[111],"five":[113],"representative":[114],"demonstrate":[116],"consistently":[119],"achieves":[120],"competitive":[121],"results":[122],"settings,":[125],"underscoring":[126],"its":[127],"potential":[128],"as":[129],"general-purpose":[131],"TSFM.":[132]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-07-04T00:00:00"}