{"id":"https://openalex.org/W2994169213","doi":"https://doi.org/10.1109/tfuzz.2019.2956904","title":"Time Series Prediction Using Sparse Autoencoder and High-Order Fuzzy Cognitive Maps","display_name":"Time Series Prediction Using Sparse Autoencoder and High-Order Fuzzy Cognitive Maps","publication_year":2019,"publication_date":"2019-12-03","ids":{"openalex":"https://openalex.org/W2994169213","doi":"https://doi.org/10.1109/tfuzz.2019.2956904","mag":"2994169213"},"language":"en","primary_location":{"id":"doi:10.1109/tfuzz.2019.2956904","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tfuzz.2019.2956904","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":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5037706636","display_name":"Kai Wu","orcid":"https://orcid.org/0000-0002-1852-6364"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kai Wu","raw_affiliation_strings":["School of Artificial Intelligence, Xidian University, Xi'an, China"],"raw_orcid":"https://orcid.org/0000-0002-1852-6364","affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100375038","display_name":"Jing Liu","orcid":"https://orcid.org/0000-0002-6834-5350"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jing Liu","raw_affiliation_strings":["School of Artificial Intelligence, Xidian University, Xi'an, China"],"raw_orcid":"https://orcid.org/0000-0002-6834-5350","affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101645402","display_name":"Penghui Liu","orcid":"https://orcid.org/0000-0003-4928-6874"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Penghui Liu","raw_affiliation_strings":["School of Artificial Intelligence, Xidian University, Xi'an, China"],"raw_orcid":"https://orcid.org/0000-0003-4928-6874","affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5008798403","display_name":"Shanchao Yang","orcid":"https://orcid.org/0000-0003-0606-5609"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shanchao Yang","raw_affiliation_strings":["School of Artificial Intelligence, Xidian University, Xi'an, China"],"raw_orcid":"https://orcid.org/0000-0003-0606-5609","affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I149594827"],"apc_list":null,"apc_paid":null,"fwci":3.6709,"has_fulltext":false,"cited_by_count":81,"citation_normalized_percentile":{"value":0.94557448,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":"28","issue":"12","first_page":"3110","last_page":"3121"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12805","display_name":"Cognitive Science and Mapping","score":0.9998999834060669,"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":0.9998999834060669,"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/T13062","display_name":"Cognitive Computing and Networks","score":0.925000011920929,"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/autoencoder","display_name":"Autoencoder","score":0.7729544043540955},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6979951858520508},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6529959440231323},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.5480067133903503},{"id":"https://openalex.org/keywords/fuzzy-logic","display_name":"Fuzzy logic","score":0.5259592533111572},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.48824718594551086},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4682560861110687},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4620596170425415},{"id":"https://openalex.org/keywords/operator","display_name":"Operator (biology)","score":0.45278969407081604},{"id":"https://openalex.org/keywords/gradient-descent","display_name":"Gradient descent","score":0.44904884696006775},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.43900877237319946},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4277767241001129},{"id":"https://openalex.org/keywords/fuzzy-cognitive-map","display_name":"Fuzzy cognitive map","score":0.42282834649086},{"id":"https://openalex.org/keywords/current","display_name":"Current (fluid)","score":0.4156973361968994},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.30102622509002686},{"id":"https://openalex.org/keywords/fuzzy-control-system","display_name":"Fuzzy control system","score":0.22722339630126953},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.20143342018127441},{"id":"https://openalex.org/keywords/neuro-fuzzy","display_name":"Neuro-fuzzy","score":0.1794009506702423}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.7729544043540955},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6979951858520508},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6529959440231323},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.5480067133903503},{"id":"https://openalex.org/C58166","wikidata":"https://www.wikidata.org/wiki/Q224821","display_name":"Fuzzy logic","level":2,"score":0.5259592533111572},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.48824718594551086},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4682560861110687},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4620596170425415},{"id":"https://openalex.org/C17020691","wikidata":"https://www.wikidata.org/wiki/Q139677","display_name":"Operator (biology)","level":5,"score":0.45278969407081604},{"id":"https://openalex.org/C153258448","wikidata":"https://www.wikidata.org/wiki/Q1199743","display_name":"Gradient descent","level":3,"score":0.44904884696006775},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.43900877237319946},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4277767241001129},{"id":"https://openalex.org/C5041914","wikidata":"https://www.wikidata.org/wiki/Q5511107","display_name":"Fuzzy cognitive map","level":5,"score":0.42282834649086},{"id":"https://openalex.org/C148043351","wikidata":"https://www.wikidata.org/wiki/Q4456944","display_name":"Current (fluid)","level":2,"score":0.4156973361968994},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.30102622509002686},{"id":"https://openalex.org/C195975749","wikidata":"https://www.wikidata.org/wiki/Q1475705","display_name":"Fuzzy control system","level":3,"score":0.22722339630126953},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.20143342018127441},{"id":"https://openalex.org/C29470771","wikidata":"https://www.wikidata.org/wiki/Q4165150","display_name":"Neuro-fuzzy","level":4,"score":0.1794009506702423},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C158448853","wikidata":"https://www.wikidata.org/wiki/Q425218","display_name":"Repressor","level":4,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C86339819","wikidata":"https://www.wikidata.org/wiki/Q407384","display_name":"Transcription factor","level":3,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tfuzz.2019.2956904","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tfuzz.2019.2956904","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"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1840888965","display_name":null,"funder_award_id":"61773300","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":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W264139406","https://openalex.org/W1971869067","https://openalex.org/W2000359198","https://openalex.org/W2015705737","https://openalex.org/W2019207321","https://openalex.org/W2025768430","https://openalex.org/W2036950209","https://openalex.org/W2049995592","https://openalex.org/W2066031679","https://openalex.org/W2081232506","https://openalex.org/W2089085970","https://openalex.org/W2094631910","https://openalex.org/W2095224843","https://openalex.org/W2119374546","https://openalex.org/W2119438891","https://openalex.org/W2121657501","https://openalex.org/W2129588477","https://openalex.org/W2133257461","https://openalex.org/W2137076380","https://openalex.org/W2141225287","https://openalex.org/W2144513243","https://openalex.org/W2148605346","https://openalex.org/W2153733695","https://openalex.org/W2159803222","https://openalex.org/W2161893161","https://openalex.org/W2344135745","https://openalex.org/W2520865593","https://openalex.org/W2535619815","https://openalex.org/W2563493533","https://openalex.org/W2566248507","https://openalex.org/W2617935313","https://openalex.org/W2749453371","https://openalex.org/W2802161886","https://openalex.org/W6679718588","https://openalex.org/W6681151457","https://openalex.org/W6683819664","https://openalex.org/W6738644762"],"related_works":["https://openalex.org/W3013693939","https://openalex.org/W2159052453","https://openalex.org/W2566616303","https://openalex.org/W3131327266","https://openalex.org/W2734887215","https://openalex.org/W4297051394","https://openalex.org/W2669956259","https://openalex.org/W4249005693","https://openalex.org/W4392946183","https://openalex.org/W3088732000"],"abstract_inverted_index":{"The":[0],"problem":[1],"of":[2,27,47,51,62,77,159,182,199,206,209,217,251,276],"time":[3,53,144,171],"series":[4,145],"prediction":[5,93,121,146,230],"based":[6,130],"on":[7,131,253],"fuzzy":[8],"cognitive":[9],"maps":[10],"(FCMs)":[11],"is":[12,30,164],"unresolved.":[13],"Although":[14],"many":[15],"methods":[16,29,57,98],"have":[17,37],"been":[18],"proposed":[19],"to":[20,64,91,141,166,189],"cope":[21],"with":[22,257],"this":[23,124,150],"issue,":[24],"the":[25,60,66,74,78,101,108,111,119,143,156,180,184,187,191,196,207,215,223,229,244,249,258,266,274,288],"performance":[26,250],"these":[28,97],"far":[31],"from":[32,110,169],"satisfactory.":[33],"Traditional":[34],"FCM-based":[35,83,175,201],"predictors":[36,84],"three":[38],"limitations.":[39,290],"First,":[40],"current":[41,56,82,160,174],"feature":[42,102],"extraction":[43,103],"operators":[44],"are":[45,232],"incapable":[46],"learning":[48],"good":[49],"representations":[50],"original":[52,170],"series.":[54,172],"Second,":[55],"use":[58],"just":[59],"output":[61,181],"FCMs":[63,109],"predict":[65],"next":[67],"value;":[68],"they":[69,114],"do":[70,115],"not":[71,116],"directly":[72],"utilize":[73],"important":[75],"information":[76],"latent":[79,112],"features.":[80],"Third,":[81],"optimize":[85,100,118,237],"each":[86],"component":[87],"individually,":[88],"thereby":[89,194],"leading":[90],"low":[92],"accuracy.":[94],"For":[95],"example,":[96],"first":[99,157],"operator":[104],"and":[105,136,186,242],"then":[106],"learn":[107],"features;":[113],"simultaneously":[117],"whole":[120,241],"model.":[122],"In":[123,203],"article,":[125],"we":[126,148,235],"develop":[127],"a":[128,132,137,240],"framework":[129,151],"sparse":[133],"autoencoder":[134],"(SAE)":[135],"high-order":[138],"FCM":[139],"(HFCM)":[140],"address":[142],"problem;":[147],"refer":[149],"as":[152,239],"SAE-FCM.":[153],"To":[154],"overcome":[155,243,287],"limitation":[158,198],"methods,":[161,265],"an":[162,204],"SAE":[163,185],"employed":[165],"extract":[167],"features":[168],"Unlike":[173],"predictors,":[176],"our":[177,277],"method":[178,227],"combines":[179],"both":[183],"HFCM":[188],"calculate":[190],"predicted":[192],"value,":[193],"overcoming":[195],"second":[197],"traditional":[200],"predictors.":[202],"application":[205],"idea":[208],"\u201cfine":[210],"tuning\u201d":[211],"in":[212],"deep":[213],"learning,":[214],"weights":[216],"SAE-FCM":[218,238,252,272,284],"can":[219,236,285],"be":[220],"updated":[221],"by":[222,262,270],"batch":[224],"gradient":[225],"descent":[226],"if":[228],"errors":[231],"great.":[233],"Thus,":[234],"third":[245],"limitation.":[246],"We":[247],"validate":[248],"ten":[254],"datasets.":[255],"Compared":[256],"experimental":[259,267],"results":[260,268],"obtained":[261,269],"using":[263,271],"state-of-the-art":[264],"demonstrate":[273],"effectiveness":[275],"method.":[278],"Extensive":[279],"experiments":[280],"also":[281],"show":[282],"that":[283],"effectively":[286],"above":[289]},"counts_by_year":[{"year":2026,"cited_by_count":8},{"year":2025,"cited_by_count":18},{"year":2024,"cited_by_count":19},{"year":2023,"cited_by_count":10},{"year":2022,"cited_by_count":14},{"year":2021,"cited_by_count":8},{"year":2020,"cited_by_count":4}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}