{"id":"https://openalex.org/W7131247518","doi":"https://doi.org/10.1109/tfuzz.2026.3667554","title":"Hybrid Offline\u2013Online Learning of Fuzzy Cognitive Maps for Forecasting Nonstationary Streaming Time Series","display_name":"Hybrid Offline\u2013Online Learning of Fuzzy Cognitive Maps for Forecasting Nonstationary Streaming Time Series","publication_year":2026,"publication_date":"2026-02-24","ids":{"openalex":"https://openalex.org/W7131247518","doi":"https://doi.org/10.1109/tfuzz.2026.3667554"},"language":"en","primary_location":{"id":"doi:10.1109/tfuzz.2026.3667554","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tfuzz.2026.3667554","pdf_url":null,"source":{"id":"https://openalex.org/S134177497","display_name":"IEEE Transactions on Fuzzy Systems","issn_l":"1063-6706","issn":["1063-6706","1941-0034"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Fuzzy Systems","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://pure.qub.ac.uk/en/publications/8864864a-b0c8-433d-8cd0-3c9566979235","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5123944319","display_name":"X. Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I3133134087","display_name":"Lanzhou Jiaotong University","ror":"https://ror.org/03144pv92","country_code":"CN","type":"education","lineage":["https://openalex.org/I3133134087"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoqian Liu","raw_affiliation_strings":["School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-0715-1376","affiliations":[{"raw_affiliation_string":"School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou, China","institution_ids":["https://openalex.org/I3133134087"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126739227","display_name":"Yingjun Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yingjun Zhang","raw_affiliation_strings":["School of Computer Science and Technology, Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence, Beijing Jiaotong University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-4279-2715","affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126704926","display_name":"Hui Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I126231945","display_name":"Queen's University Belfast","ror":"https://ror.org/00hswnk62","country_code":"GB","type":"education","lineage":["https://openalex.org/I126231945"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Hui Wang","raw_affiliation_strings":["School of Electronics, Electrical Engineering, and Computer Science, Queen’s University, Belfast, U.K"],"raw_orcid":"https://orcid.org/0000-0003-2633-6015","affiliations":[{"raw_affiliation_string":"School of Electronics, Electrical Engineering, and Computer Science, Queen’s University, Belfast, U.K","institution_ids":["https://openalex.org/I126231945"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080941332","display_name":"Wenqi Wan","orcid":null},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenqi Wan","raw_affiliation_strings":["School of Computer Science and Technology, Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence, Beijing Jiaotong University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5106670276","display_name":"Jianxing Liu","orcid":"https://orcid.org/0000-0002-2201-3887"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiang Liu","raw_affiliation_strings":["School of Automation and Intelligence, Beijing Jiaotong University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-2836-7651","affiliations":[{"raw_affiliation_string":"School of Automation and Intelligence, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100636115","display_name":"Baigen Cai","orcid":"https://orcid.org/0000-0002-6440-005X"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bai-gen Cai","raw_affiliation_strings":["School of Automation and Intelligence, Beijing Jiaotong University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-6440-005X","affiliations":[{"raw_affiliation_string":"School of Automation and Intelligence, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.18361311,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"34","issue":"5","first_page":"1615","last_page":"1629"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12805","display_name":"Cognitive Science and Mapping","score":0.9162999987602234,"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"}},"topics":[{"id":"https://openalex.org/T12805","display_name":"Cognitive Science and Mapping","score":0.9162999987602234,"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":0.01590000092983246,"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/T10320","display_name":"Neural Networks and Applications","score":0.012799999676644802,"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/fuzzy-cognitive-map","display_name":"Fuzzy cognitive map","score":0.7253999710083008},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.6761999726295471},{"id":"https://openalex.org/keywords/sliding-window-protocol","display_name":"Sliding window protocol","score":0.6266000270843506},{"id":"https://openalex.org/keywords/adaptability","display_name":"Adaptability","score":0.5954999923706055},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.499099999666214},{"id":"https://openalex.org/keywords/offline-learning","display_name":"Offline learning","score":0.49410000443458557},{"id":"https://openalex.org/keywords/fuzzy-logic","display_name":"Fuzzy logic","score":0.48240000009536743},{"id":"https://openalex.org/keywords/concept-drift","display_name":"Concept drift","score":0.3513999879360199},{"id":"https://openalex.org/keywords/data-stream","display_name":"Data stream","score":0.33730000257492065}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7996000051498413},{"id":"https://openalex.org/C5041914","wikidata":"https://www.wikidata.org/wiki/Q5511107","display_name":"Fuzzy cognitive map","level":5,"score":0.7253999710083008},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.6761999726295471},{"id":"https://openalex.org/C102392041","wikidata":"https://www.wikidata.org/wiki/Q592860","display_name":"Sliding window protocol","level":3,"score":0.6266000270843506},{"id":"https://openalex.org/C177606310","wikidata":"https://www.wikidata.org/wiki/Q5674297","display_name":"Adaptability","level":2,"score":0.5954999923706055},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.558899998664856},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.499099999666214},{"id":"https://openalex.org/C2780490138","wikidata":"https://www.wikidata.org/wiki/Q7079636","display_name":"Offline learning","level":3,"score":0.49410000443458557},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4918999969959259},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.48260000348091125},{"id":"https://openalex.org/C58166","wikidata":"https://www.wikidata.org/wiki/Q224821","display_name":"Fuzzy logic","level":2,"score":0.48240000009536743},{"id":"https://openalex.org/C60777511","wikidata":"https://www.wikidata.org/wiki/Q3045002","display_name":"Concept drift","level":3,"score":0.3513999879360199},{"id":"https://openalex.org/C2778484313","wikidata":"https://www.wikidata.org/wiki/Q1172540","display_name":"Data stream","level":2,"score":0.33730000257492065},{"id":"https://openalex.org/C89198739","wikidata":"https://www.wikidata.org/wiki/Q3079880","display_name":"Data stream mining","level":2,"score":0.3296999931335449},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3276999890804291},{"id":"https://openalex.org/C89611455","wikidata":"https://www.wikidata.org/wiki/Q6804646","display_name":"Mechanism (biology)","level":2,"score":0.3246000111103058},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.32269999384880066},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.31450000405311584},{"id":"https://openalex.org/C197298091","wikidata":"https://www.wikidata.org/wiki/Q5318963","display_name":"Dynamic data","level":2,"score":0.303600013256073},{"id":"https://openalex.org/C9936470","wikidata":"https://www.wikidata.org/wiki/Q6510405","display_name":"Least-squares function approximation","level":3,"score":0.2842999994754791},{"id":"https://openalex.org/C2777611316","wikidata":"https://www.wikidata.org/wiki/Q39045282","display_name":"Streaming data","level":2,"score":0.2754000127315521},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2741999924182892},{"id":"https://openalex.org/C175706884","wikidata":"https://www.wikidata.org/wiki/Q1130194","display_name":"Moving average","level":2,"score":0.2667999863624573},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.26660001277923584},{"id":"https://openalex.org/C2778751112","wikidata":"https://www.wikidata.org/wiki/Q835016","display_name":"Window (computing)","level":2,"score":0.26440000534057617},{"id":"https://openalex.org/C196921405","wikidata":"https://www.wikidata.org/wiki/Q786431","display_name":"Online algorithm","level":2,"score":0.26019999384880066},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.25940001010894775},{"id":"https://openalex.org/C2987525970","wikidata":"https://www.wikidata.org/wiki/Q96374569","display_name":"Causal analysis","level":2,"score":0.25870001316070557},{"id":"https://openalex.org/C196340769","wikidata":"https://www.wikidata.org/wiki/Q7698910","display_name":"Temporal difference learning","level":3,"score":0.2533000111579895}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tfuzz.2026.3667554","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tfuzz.2026.3667554","pdf_url":null,"source":{"id":"https://openalex.org/S134177497","display_name":"IEEE Transactions on Fuzzy Systems","issn_l":"1063-6706","issn":["1063-6706","1941-0034"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Fuzzy Systems","raw_type":"journal-article"},{"id":"pmh:oai:pure.qub.ac.uk/portal:publications/8864864a-b0c8-433d-8cd0-3c9566979235","is_oa":true,"landing_page_url":"https://pure.qub.ac.uk/en/publications/8864864a-b0c8-433d-8cd0-3c9566979235","pdf_url":null,"source":{"id":"https://openalex.org/S4306402319","display_name":"Research Portal (Queen's University Belfast)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I126231945","host_organization_name":"Queen's University Belfast","host_organization_lineage":["https://openalex.org/I126231945"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Liu, X, Zhang, Y, Wang, H, Wan, W, Liu, J & Cai, B 2026, 'Hybrid offline-online learning of fuzzy cognitive maps for forecasting nonstationary streaming time series', IEEE Transactions on Fuzzy Systems. https://doi.org/10.1109/TFUZZ.2026.3667554","raw_type":"info:eu-repo/semantics/article"}],"best_oa_location":{"id":"pmh:oai:pure.qub.ac.uk/portal:publications/8864864a-b0c8-433d-8cd0-3c9566979235","is_oa":true,"landing_page_url":"https://pure.qub.ac.uk/en/publications/8864864a-b0c8-433d-8cd0-3c9566979235","pdf_url":null,"source":{"id":"https://openalex.org/S4306402319","display_name":"Research Portal (Queen's University Belfast)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I126231945","host_organization_name":"Queen's University Belfast","host_organization_lineage":["https://openalex.org/I126231945"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Liu, X, Zhang, Y, Wang, H, Wan, W, Liu, J & Cai, B 2026, 'Hybrid offline-online learning of fuzzy cognitive maps for forecasting nonstationary streaming time series', IEEE Transactions on Fuzzy Systems. https://doi.org/10.1109/TFUZZ.2026.3667554","raw_type":"info:eu-repo/semantics/article"},"sustainable_development_goals":[{"score":0.47263598442077637,"id":"https://metadata.un.org/sdg/13","display_name":"Climate action"}],"awards":[{"id":"https://openalex.org/G5600642901","display_name":null,"funder_award_id":"U2268206","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"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":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Fuzzy":[0],"Cognitive":[1],"Maps":[2],"(FCMs)":[3],"are":[4,211],"a":[5,105,114,119,153,169,197],"prominent":[6],"soft":[7],"computing":[8],"technique":[9],"for":[10,15,69],"time":[11,46,87,251],"series":[12,47,88,252],"forecasting,":[13],"valued":[14],"their":[16,56],"ability":[17],"to":[18,58,92,147,219,226],"effectively":[19],"model":[20,163,218],"complex":[21],"temporal":[22],"dynamics.":[23],"While":[24],"FCM":[25,109],"learning":[26,110],"algorithms":[27],"improve":[28],"the":[29,82,93,126,139,162,193,217],"performance":[30,95,244],"of":[31,77,85,96,203],"FCM-based":[32,97,131],"predictors":[33],"by":[34,168],"capturing":[35],"causal":[36,79,150],"relationships":[37,151],"between":[38],"nodes,":[39],"existing":[40],"approaches":[41],"predominantly":[42],"rely":[43],"on":[44,174,231],"offline":[45,127],"data":[48,72,181,205],"stored":[49],"in":[50,61,249,253],"static":[51],"repositories.":[52],"This":[53],"limitation":[54],"hinders":[55],"adaptability":[57],"dynamic":[59,75],"changes":[60],"map":[62],"structures":[63],"over":[64],"time,":[65],"making":[66],"them":[67],"unsuitable":[68],"real-time":[70],"streaming":[71,165],"analysis":[73],"and":[74,186,233,245],"modeling":[76],"evolving":[78],"relationships.":[80],"Furthermore,":[81],"non-stationary":[83,234,250],"nature":[84],"real-world":[86],"presents":[89],"significant":[90],"challenges":[91],"predictive":[94],"models.":[98],"To":[99,189],"overcome":[100],"these":[101,209],"limitations,":[102],"we":[103],"propose":[104],"novel":[106],"hybrid":[107],"offline-online":[108],"algorithm":[111],"that":[112,237],"integrates":[113],"non-stationarity":[115,170],"detection":[116,171],"mechanism":[117,172,179],"with":[118],"knowledge-guided":[120],"least":[121,141],"squares":[122,142],"(KGLS)":[123],"method.":[124],"In":[125],"phase,":[128],"an":[129],"initial":[130],"predictor":[132],"is":[133,145],"constructed":[134],"from":[135],"historical":[136],"data,":[137,166,215],"where":[138],"recursive":[140],"(RLS)":[143],"method":[144,195,239],"employed":[146],"capture":[148],"long-term":[149],"using":[152,164],"sliding":[154],"window":[155],"technique.":[156],"The":[157,178],"online":[158],"phase":[159],"incrementally":[160],"updates":[161],"guided":[167],"based":[173],"statistical":[175],"hypothesis":[176],"testing.":[177],"classifies":[180],"shifts":[182],"into":[183],"stable,":[184],"warning,":[185],"drift":[187],"levels.":[188],"mitigate":[190],"catastrophic":[191],"forgetting,":[192],"KGLS":[194],"maintains":[196],"compact":[198],"yet":[199],"representative":[200],"memory":[201],"buffer":[202],"past":[204],"samples.":[206],"During":[207],"training,":[208],"samples":[210],"replayed":[212],"alongside":[213],"new":[214,227],"enabling":[216],"reinforce":[220],"previously":[221],"learned":[222],"patterns":[223],"while":[224],"adapting":[225],"information.":[228],"Extensive":[229],"experiments":[230],"stationary":[232],"datasets":[235],"demonstrate":[236],"our":[238],"achieves":[240],"superior":[241],"overall":[242],"prediction":[243],"accurately":[246],"forecasts":[247],"trends":[248],"real":[254],"time.":[255]},"counts_by_year":[],"updated_date":"2026-05-07T06:04:25.777469","created_date":"2026-02-25T00:00:00"}