{"id":"https://openalex.org/W7163217854","doi":"https://doi.org/10.48550/arxiv.2606.01306","title":"FAiT: Frequency-Aware Inverted Transformer for Multivariate Time Series Forecasting","display_name":"FAiT: Frequency-Aware Inverted Transformer for Multivariate Time Series Forecasting","publication_year":2026,"publication_date":"2026-05-31","ids":{"openalex":"https://openalex.org/W7163217854","doi":"https://doi.org/10.48550/arxiv.2606.01306"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.01306","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.01306","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.2606.01306","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5137640268","display_name":"Peng He","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"He, Peng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137632629","display_name":"Yao Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Yao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137656636","display_name":"Yanglei Gan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gan, Yanglei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137683667","display_name":"Run Lin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lin, Run","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137648684","display_name":"Yuxiang Cai","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cai, Yuxiang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5137652259","display_name":"Qiao Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Qiao","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":0.3312999904155731,"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":0.3312999904155731,"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.13249999284744263,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11918","display_name":"Forecasting Techniques and Applications","score":0.1145000010728836,"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/smoothing","display_name":"Smoothing","score":0.6901999711990356},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.5471000075340271},{"id":"https://openalex.org/keywords/property","display_name":"Property (philosophy)","score":0.44769999384880066},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.44670000672340393},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.42739999294281006},{"id":"https://openalex.org/keywords/operator","display_name":"Operator (biology)","score":0.4253000020980835},{"id":"https://openalex.org/keywords/core","display_name":"Core (optical fiber)","score":0.37070000171661377}],"concepts":[{"id":"https://openalex.org/C3770464","wikidata":"https://www.wikidata.org/wiki/Q775963","display_name":"Smoothing","level":2,"score":0.6901999711990356},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.5471000075340271},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5407000184059143},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5109999775886536},{"id":"https://openalex.org/C189950617","wikidata":"https://www.wikidata.org/wiki/Q937228","display_name":"Property (philosophy)","level":2,"score":0.44769999384880066},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.44670000672340393},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.42739999294281006},{"id":"https://openalex.org/C17020691","wikidata":"https://www.wikidata.org/wiki/Q139677","display_name":"Operator (biology)","level":5,"score":0.4253000020980835},{"id":"https://openalex.org/C2164484","wikidata":"https://www.wikidata.org/wiki/Q5170150","display_name":"Core (optical fiber)","level":2,"score":0.37070000171661377},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.3637000024318695},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.35580000281333923},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.32339999079704285},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.3122999966144562},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.3041999936103821},{"id":"https://openalex.org/C123079801","wikidata":"https://www.wikidata.org/wiki/Q750240","display_name":"Modulation (music)","level":2,"score":0.2842000126838684},{"id":"https://openalex.org/C175291020","wikidata":"https://www.wikidata.org/wiki/Q1156822","display_name":"Offset (computer science)","level":2,"score":0.28360000252723694},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.26429998874664307},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.2599000036716461},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.25949999690055847},{"id":"https://openalex.org/C2781170535","wikidata":"https://www.wikidata.org/wiki/Q30587856","display_name":"Noisy data","level":2,"score":0.2508000135421753}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.01306","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.01306","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.2606.01306","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.01306","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":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"While":[0],"Transformer-based":[1,178],"architectures":[2],"have":[3,38],"established":[4],"themselves":[5],"as":[6,22,118],"a":[7,23,63,69,99,119,125],"dominant":[8],"paradigm":[9],"in":[10],"Multivariate":[11],"Time":[12],"Series":[13],"Forecasting":[14],"(MTSF),":[15],"their":[16,76],"core":[17],"self-attention":[18],"mechanism":[19],"inherently":[20],"functions":[21],"low-pass":[24,121],"filter,":[25],"systematically":[26],"smoothing":[27],"out":[28],"high-frequency":[29],"signals":[30],"vital":[31],"for":[32,87],"sharp":[33],"local":[34],"changes.":[35],"Recent":[36],"advancements":[37],"increasingly":[39],"incorporated":[40],"frequency-domain":[41],"operations":[42],"to":[43,135,151],"address":[44],"this":[45],"bias,":[46],"however,":[47],"most":[48],"existing":[49],"designs":[50],"rely":[51],"on":[52,168],"fixed":[53],"spectral":[54,77,107,157],"bases":[55],"and":[56,123,179],"apply":[57],"sequence-wise":[58],"(uniform)":[59],"modulation,":[60],"implicitly":[61],"assuming":[62],"time-invariant":[64],"frequency":[65],"response.":[66],"This":[67],"overlooks":[68],"key":[70],"property":[71],"of":[72,156],"real-world":[73],"series":[74],"that":[75,173],"characteristics":[78],"often":[79],"evolve":[80],"over":[81,162],"time,":[82],"making":[83],"uniform":[84],"modulation":[85],"insufficient":[86],"capturing":[88],"fine-grained":[89,160],"temporal":[90],"dynamics.":[91],"To":[92],"tackle":[93],"these":[94],"limitations,":[95],"we":[96],"propose":[97],"FAiT,":[98],"Frequency-Aware":[100],"inverted":[101],"Transformer.":[102],"Specifically,":[103],"FAiT":[104,141,174],"rectifies":[105],"the":[106,115,132,154],"bias":[108],"internally":[109],"through":[110],"Inverted":[111],"Attention,":[112],"which":[113,147],"interprets":[114],"attention":[116,133],"map":[117],"learnable":[120],"operator":[122],"constructs":[124],"dedicated":[126],"complementary":[127],"high-pass":[128],"branch":[129],"by":[130],"inverting":[131],"matrix":[134],"recover":[136],"attenuated":[137],"transient":[138],"signals.":[139],"Furthermore,":[140],"introduces":[142],"Dynamic":[143],"Temporal-Frequency":[144],"Modulation":[145],"(DTFM),":[146],"synthesizes":[148],"instance-conditioned":[149],"weights":[150],"adaptively":[152],"re-calibrate":[153],"energy":[155],"sub-bands,":[158],"enabling":[159],"control":[161],"evolving":[163],"multi-scale":[164],"patterns.":[165],"Extensive":[166],"experiments":[167],"widely":[169],"used":[170],"benchmarks":[171],"demonstrate":[172],"consistently":[175],"outperforms":[176],"state-of-the-art":[177],"frequency-enhanced":[180],"baselines,":[181],"while":[182],"maintaining":[183],"computational":[184],"efficiency.":[185]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-03T00:00:00"}
