{"id":"https://openalex.org/W7125936170","doi":"https://doi.org/10.1109/smc58881.2025.11343366","title":"ACET: An Adaptive Component Extraction and Tokenization Framework for Time Series Forecasting","display_name":"ACET: An Adaptive Component Extraction and Tokenization Framework for Time Series Forecasting","publication_year":2025,"publication_date":"2025-10-05","ids":{"openalex":"https://openalex.org/W7125936170","doi":"https://doi.org/10.1109/smc58881.2025.11343366"},"language":null,"primary_location":{"id":"doi:10.1109/smc58881.2025.11343366","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc58881.2025.11343366","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5001335996","display_name":"Jiexuan Cai","orcid":null},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jiexuan Cai","raw_affiliation_strings":["Tongji University,College of Electronics and Information Engineering,China"],"affiliations":[{"raw_affiliation_string":"Tongji University,College of Electronics and Information Engineering,China","institution_ids":["https://openalex.org/I116953780"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124097768","display_name":"Tianyi Yin","orcid":null},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tianyi Yin","raw_affiliation_strings":["Tongji University,College of Electronics and Information Engineering,China"],"affiliations":[{"raw_affiliation_string":"Tongji University,College of Electronics and Information Engineering,China","institution_ids":["https://openalex.org/I116953780"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124111577","display_name":"Jingwei Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jingwei Wang","raw_affiliation_strings":["Tongji University,College of Electronics and Information Engineering,China"],"affiliations":[{"raw_affiliation_string":"Tongji University,College of Electronics and Information Engineering,China","institution_ids":["https://openalex.org/I116953780"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5121446375","display_name":"Chenze Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chenze Wang","raw_affiliation_strings":["Tongji University,College of Electronics and Information Engineering,China"],"affiliations":[{"raw_affiliation_string":"Tongji University,College of Electronics and Information Engineering,China","institution_ids":["https://openalex.org/I116953780"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063772112","display_name":"Yukai Zhao","orcid":"https://orcid.org/0000-0001-8924-3356"},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yukai Zhao","raw_affiliation_strings":["Tongji University,College of Electronics and Information Engineering,China"],"affiliations":[{"raw_affiliation_string":"Tongji University,College of Electronics and Information Engineering,China","institution_ids":["https://openalex.org/I116953780"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124085295","display_name":"Yunlong Ma","orcid":null},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yunlong Ma","raw_affiliation_strings":["Tongji University,College of Electronics and Information Engineering,China"],"affiliations":[{"raw_affiliation_string":"Tongji University,College of Electronics and Information Engineering,China","institution_ids":["https://openalex.org/I116953780"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5124083339","display_name":"Min Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Min Liu","raw_affiliation_strings":["Tongji University,College of Electronics and Information Engineering,China"],"affiliations":[{"raw_affiliation_string":"Tongji University,College of Electronics and Information Engineering,China","institution_ids":["https://openalex.org/I116953780"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5001335996"],"corresponding_institution_ids":["https://openalex.org/I116953780"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.76399748,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"147","last_page":"152"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.2329999953508377,"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/T11326","display_name":"Stock Market Forecasting Methods","score":0.2329999953508377,"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":0.2321999967098236,"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":0.19429999589920044,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/lexical-analysis","display_name":"Lexical analysis","score":0.7623000144958496},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5785999894142151},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.5343000292778015},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.45730000734329224},{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"Principal component analysis","score":0.4352000057697296},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.40549999475479126},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.40540000796318054},{"id":"https://openalex.org/keywords/quantization","display_name":"Quantization (signal processing)","score":0.387800008058548}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8163999915122986},{"id":"https://openalex.org/C176982825","wikidata":"https://www.wikidata.org/wiki/Q835922","display_name":"Lexical analysis","level":2,"score":0.7623000144958496},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5785999894142151},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.5343000292778015},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.517300009727478},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5080000162124634},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.45730000734329224},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.4352000057697296},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.40549999475479126},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.40540000796318054},{"id":"https://openalex.org/C28855332","wikidata":"https://www.wikidata.org/wiki/Q198099","display_name":"Quantization (signal processing)","level":2,"score":0.387800008058548},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.36899998784065247},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.3662000000476837},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.357699990272522},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.349700003862381},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.3352000117301941},{"id":"https://openalex.org/C51432778","wikidata":"https://www.wikidata.org/wiki/Q1259145","display_name":"Independent component analysis","level":2,"score":0.3278000056743622},{"id":"https://openalex.org/C199163554","wikidata":"https://www.wikidata.org/wiki/Q1681619","display_name":"Univariate","level":3,"score":0.3230000138282776},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.30709999799728394},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.28780001401901245},{"id":"https://openalex.org/C195807954","wikidata":"https://www.wikidata.org/wiki/Q1662562","display_name":"Information extraction","level":2,"score":0.2793000042438507},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.2614000141620636},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.26109999418258667},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.25450000166893005},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.2540000081062317}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/smc58881.2025.11343366","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc58881.2025.11343366","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W2988244882","https://openalex.org/W3177318507","https://openalex.org/W4283706581","https://openalex.org/W4292672211","https://openalex.org/W4296519619","https://openalex.org/W4309675319","https://openalex.org/W4312713717","https://openalex.org/W4390096599","https://openalex.org/W4393147362","https://openalex.org/W4393224374","https://openalex.org/W4399957366","https://openalex.org/W4405316559","https://openalex.org/W4406611982","https://openalex.org/W4406612708","https://openalex.org/W4409362973"],"related_works":[],"abstract_inverted_index":{"Language":[0],"models":[1],"have":[2],"been":[3],"proven":[4],"to":[5,29,38,48,71,174],"handle":[6],"time":[7,25,132],"series":[8,26,133],"data":[9,27,134],"after":[10],"tokenization":[11,117],"and":[12,33,41,62,81,169],"show":[13],"generalization":[14],"performance":[15],"on":[16,152],"unseen":[17],"forecasting":[18,160],"tasks.":[19],"However,":[20],"existing":[21],"techniques":[22],"for":[23,105,146],"tokenizing":[24],"struggle":[28],"eliminate":[30],"redundant":[31],"information":[32,143],"noise,":[34],"which":[35,66],"can":[36],"lead":[37],"signal":[39,116],"aliasing":[40],"cumulative":[42],"quantization":[43],"errors,":[44],"making":[45],"it":[46],"difficult":[47],"further":[49],"improve":[50],"prediction":[51],"performance.":[52],"In":[53],"this":[54],"paper,":[55],"we":[56],"propose":[57],"an":[58],"Adaptive":[59],"Component":[60,77],"Extraction":[61,78],"Tokenization":[63,85],"(ACET)":[64],"framework,":[65],"includes":[67],"two":[68],"key":[69],"novelties":[70],"address":[72],"these":[73],"challenges:":[74],"the":[75,82,92,96,99,103,113,121,128],"Dynamic":[76],"Module":[79,86],"(DCEM)":[80],"Time":[83],"Series":[84],"(TSTM).":[87],"The":[88],"DCEM":[89],"dynamically":[90],"isolates":[91],"principal":[93],"components":[94],"from":[95],"interference":[97],"in":[98,158,167,171],"original":[100],"signal,":[101],"eliminating":[102],"need":[104],"manual":[106],"parameter":[107],"tuning.":[108],"This":[109],"not":[110],"only":[111],"enhances":[112],"accuracy":[114],"of":[115,124,165],"but":[118],"also":[119],"mitigates":[120],"adverse":[122],"effects":[123],"high-frequency":[125],"noise.":[126],"Then,":[127],"TSTM":[129],"tokenizes":[130],"continuous":[131],"while":[135],"preserving":[136],"long-term":[137],"trend":[138],"features,":[139],"ensuring":[140],"that":[141],"critical":[142],"is":[144],"retained":[145],"subsequent":[147],"forecasting.":[148],"Extensive":[149],"cross-domain":[150],"experiments":[151],"various":[153],"real-world":[154],"datasets":[155],"demonstrate":[156],"that,":[157],"zero-shot":[159],"scenarios,":[161],"ACET":[162],"achieves":[163],"improvements":[164],"19.27%":[166],"WQL":[168],"6.63%":[170],"MASE,":[172],"compared":[173],"baseline":[175],"methods.":[176]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2026-01-29T00:00:00"}
