{"id":"https://openalex.org/W7133341385","doi":"https://doi.org/10.48550/arxiv.2603.00037","title":"StaTS: Spectral Trajectory Schedule Learning for Adaptive Time Series Forecasting with Frequency Guided Denoiser","display_name":"StaTS: Spectral Trajectory Schedule Learning for Adaptive Time Series Forecasting with Frequency Guided Denoiser","publication_year":2026,"publication_date":"2026-02-08","ids":{"openalex":"https://openalex.org/W7133341385","doi":"https://doi.org/10.48550/arxiv.2603.00037"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.00037","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.00037","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":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.00037","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5127990449","display_name":"Jintao Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Jintao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5127888910","display_name":"Zirui Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Zirui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5127990763","display_name":"Mingyue Cheng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cheng, Mingyue","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058613427","display_name":"Xianquan Wang","orcid":"https://orcid.org/0009-0003-9744-220X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Xianquan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Liu, Zhiding","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Zhiding","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5127932290","display_name":"Qi Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Qi","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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.3089999854564667,"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"}},"topics":[{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.3089999854564667,"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/T11326","display_name":"Stock Market Forecasting Methods","score":0.12070000171661377,"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":0.08959999680519104,"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/schedule","display_name":"Schedule","score":0.6682000160217285},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.6391000151634216},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5631999969482422},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.5324000120162964},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.4569999873638153},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.4018000066280365},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.38359999656677246},{"id":"https://openalex.org/keywords/scheduling","display_name":"Scheduling (production processes)","score":0.33629998564720154}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6819999814033508},{"id":"https://openalex.org/C68387754","wikidata":"https://www.wikidata.org/wiki/Q7271585","display_name":"Schedule","level":2,"score":0.6682000160217285},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.6391000151634216},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5631999969482422},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.5324000120162964},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.4569999873638153},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4327000081539154},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.4018000066280365},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.38359999656677246},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3626999855041504},{"id":"https://openalex.org/C206729178","wikidata":"https://www.wikidata.org/wiki/Q2271896","display_name":"Scheduling (production processes)","level":2,"score":0.33629998564720154},{"id":"https://openalex.org/C52740198","wikidata":"https://www.wikidata.org/wiki/Q1539564","display_name":"Importance sampling","level":3,"score":0.32679998874664307},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.3188999891281128},{"id":"https://openalex.org/C103824480","wikidata":"https://www.wikidata.org/wiki/Q185889","display_name":"Time domain","level":2,"score":0.30070000886917114},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.2928999960422516},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.29269999265670776},{"id":"https://openalex.org/C2776459999","wikidata":"https://www.wikidata.org/wiki/Q2119376","display_name":"Fidelity","level":2,"score":0.27950000762939453},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.2791000008583069},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.2775000035762787},{"id":"https://openalex.org/C131584629","wikidata":"https://www.wikidata.org/wiki/Q4308705","display_name":"Coupling (piping)","level":2,"score":0.26030001044273376},{"id":"https://openalex.org/C126780896","wikidata":"https://www.wikidata.org/wiki/Q899871","display_name":"Distortion (music)","level":4,"score":0.2565999925136566}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.00037","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.00037","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.00037","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.00037","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":"Preprint"},"sustainable_development_goals":[{"display_name":"Life in Land","score":0.5295270085334778,"id":"https://metadata.un.org/sdg/15"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Diffusion":[0],"models":[1],"have":[2],"been":[3],"used":[4],"for":[5,66,123],"probabilistic":[6,67],"time":[7,43,68],"series":[8,69],"forecasting":[9,70],"and":[10,27,46,76,102,105,116,129,142],"show":[11,151],"strong":[12,156],"potential.":[13],"However,":[14],"fixed":[15],"noise":[16,36,58,74,93],"schedules":[17],"often":[18],"produce":[19],"intermediate":[20],"states":[21],"that":[22,31,71,88,110],"are":[23],"hard":[24],"to":[25,98,119],"invert":[26],"a":[28,63,90],"terminal":[29],"state":[30],"deviates":[32],"from":[33],"the":[34,73,77,137],"near":[35],"assumption.":[37],"Meanwhile,":[38],"prior":[39],"methods":[40],"rely":[41],"on":[42,146],"domain":[44],"conditioning":[45],"seldom":[47],"model":[48,65],"schedule":[49,75,94,112,140],"induced":[50,113],"spectral":[51,96,114],"degradation,":[52],"which":[53],"limits":[54],"structure":[55],"recovery":[56],"across":[57,126],"levels.":[59],"We":[60],"propose":[61],"StaTS,":[62],"diffusion":[64,127],"learns":[72,89],"denoiser":[78,143],"through":[79],"alternating":[80],"updates.":[81],"StaTS":[82],"includes":[83],"Spectral":[84],"Trajectory":[85],"Scheduler":[86],"(STS)":[87],"data":[91],"adaptive":[92],"with":[95,158],"regularization":[97],"improve":[99],"structural":[100],"preservation":[101],"stepwise":[103],"invertibility,":[104],"Frequency":[106],"Guided":[107],"Denoiser":[108],"(FGD)":[109],"estimates":[111],"distortion":[115],"uses":[117],"it":[118],"modulate":[120],"denoising":[121],"strength":[122],"heterogeneous":[124],"restoration":[125],"steps":[128],"variables.":[130],"A":[131],"two":[132],"stage":[133],"training":[134],"procedure":[135],"stabilizes":[136],"coupling":[138],"between":[139],"learning":[141],"optimization.":[144],"Experiments":[145],"multiple":[147],"real":[148],"world":[149],"benchmarks":[150],"consistent":[152],"gains,":[153],"while":[154],"maintaining":[155],"performance":[157],"fewer":[159],"sampling":[160],"steps.":[161],"Our":[162],"code":[163],"is":[164],"available":[165],"at":[166],"https://github.com/zjt-gpu/StaTS/.":[167]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-03-04T00:00:00"}
