{"id":"https://openalex.org/W4409762055","doi":"https://doi.org/10.1109/access.2025.3564135","title":"An Interpretable Combined Forecasting Method for Stock Market Based on Fuzzy Time Series Model and Linear-Trend Fuzzy Information Granulation","display_name":"An Interpretable Combined Forecasting Method for Stock Market Based on Fuzzy Time Series Model and Linear-Trend Fuzzy Information Granulation","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4409762055","doi":"https://doi.org/10.1109/access.2025.3564135"},"language":"en","primary_location":{"id":"doi:10.1109/access.2025.3564135","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3564135","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2025.3564135","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5061977979","display_name":"Jindong Feng","orcid":"https://orcid.org/0000-0003-2188-4362"},"institutions":[{"id":"https://openalex.org/I68986083","display_name":"Northwest Normal University","ror":"https://ror.org/00gx3j908","country_code":"CN","type":"education","lineage":["https://openalex.org/I68986083"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jindong Feng","raw_affiliation_strings":["College of Mathematics and Statistics, Northwest Normal University, Lanzhou, China"],"raw_orcid":"https://orcid.org/0000-0003-2188-4362","affiliations":[{"raw_affiliation_string":"College of Mathematics and Statistics, Northwest Normal University, Lanzhou, China","institution_ids":["https://openalex.org/I68986083"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5057608131","display_name":"Zengtai Gong","orcid":"https://orcid.org/0000-0001-5878-1506"},"institutions":[{"id":"https://openalex.org/I68986083","display_name":"Northwest Normal University","ror":"https://ror.org/00gx3j908","country_code":"CN","type":"education","lineage":["https://openalex.org/I68986083"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zengtai Gong","raw_affiliation_strings":["College of Mathematics and Statistics, Northwest Normal University, Lanzhou, China"],"raw_orcid":"https://orcid.org/0000-0001-5878-1506","affiliations":[{"raw_affiliation_string":"College of Mathematics and Statistics, Northwest Normal University, Lanzhou, China","institution_ids":["https://openalex.org/I68986083"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5061977979"],"corresponding_institution_ids":["https://openalex.org/I68986083"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":5.3638,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.95018423,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":99},"biblio":{"volume":"13","issue":null,"first_page":"73722","last_page":"73734"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.8977000117301941,"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":0.8977000117301941,"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/T13832","display_name":"Advanced Decision-Making Techniques","score":0.8319000005722046,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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-logic","display_name":"Fuzzy logic","score":0.6778808236122131},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.6341880559921265},{"id":"https://openalex.org/keywords/granulation","display_name":"Granulation","score":0.5977140665054321},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5815398097038269},{"id":"https://openalex.org/keywords/stock-market","display_name":"Stock market","score":0.5526300072669983},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.5375352501869202},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.49394065141677856},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.4288371205329895},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4269346594810486},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.28081685304641724},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2716842293739319},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.09180799126625061}],"concepts":[{"id":"https://openalex.org/C58166","wikidata":"https://www.wikidata.org/wiki/Q224821","display_name":"Fuzzy logic","level":2,"score":0.6778808236122131},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.6341880559921265},{"id":"https://openalex.org/C88463166","wikidata":"https://www.wikidata.org/wiki/Q1543243","display_name":"Granulation","level":2,"score":0.5977140665054321},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5815398097038269},{"id":"https://openalex.org/C2780299701","wikidata":"https://www.wikidata.org/wiki/Q475000","display_name":"Stock market","level":3,"score":0.5526300072669983},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.5375352501869202},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.49394065141677856},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.4288371205329895},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4269346594810486},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.28081685304641724},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2716842293739319},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.09180799126625061},{"id":"https://openalex.org/C187320778","wikidata":"https://www.wikidata.org/wiki/Q1349130","display_name":"Geotechnical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C2780762169","wikidata":"https://www.wikidata.org/wiki/Q5905368","display_name":"Horse","level":2,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2025.3564135","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3564135","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:61d134ed16774b0ba2a3894148731f22","is_oa":true,"landing_page_url":"https://doaj.org/article/61d134ed16774b0ba2a3894148731f22","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 13, Pp 73722-73734 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2025.3564135","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3564135","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G7768183542","display_name":null,"funder_award_id":"12471444","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":39,"referenced_works":["https://openalex.org/W1971869067","https://openalex.org/W1979464351","https://openalex.org/W1993503008","https://openalex.org/W1995450389","https://openalex.org/W2001339767","https://openalex.org/W2018113971","https://openalex.org/W2024184269","https://openalex.org/W2030103549","https://openalex.org/W2034556731","https://openalex.org/W2056650703","https://openalex.org/W2060907774","https://openalex.org/W2065410941","https://openalex.org/W2069454342","https://openalex.org/W2076628063","https://openalex.org/W2109104903","https://openalex.org/W2153676086","https://openalex.org/W2165094119","https://openalex.org/W2168577773","https://openalex.org/W2550257869","https://openalex.org/W2781551123","https://openalex.org/W2997687596","https://openalex.org/W3025174351","https://openalex.org/W3032638652","https://openalex.org/W3155882000","https://openalex.org/W3156565247","https://openalex.org/W3159847795","https://openalex.org/W3208605024","https://openalex.org/W4200010759","https://openalex.org/W4211007335","https://openalex.org/W4220833154","https://openalex.org/W4281936631","https://openalex.org/W4298148503","https://openalex.org/W4299870824","https://openalex.org/W4323022404","https://openalex.org/W4324135187","https://openalex.org/W4361002120","https://openalex.org/W4361018931","https://openalex.org/W4396852908","https://openalex.org/W4406647028"],"related_works":["https://openalex.org/W2123804754","https://openalex.org/W1485197043","https://openalex.org/W2895629587","https://openalex.org/W2025546910","https://openalex.org/W1995249265","https://openalex.org/W2467996332","https://openalex.org/W2119012848","https://openalex.org/W2622688551","https://openalex.org/W1550175370","https://openalex.org/W1990205660"],"abstract_inverted_index":{"Stock":[0],"market":[1,202],"forecasting":[2,25,156,175],"demands":[3],"models":[4,55],"that":[5,27,106,168],"balance":[6],"high":[7],"accuracy":[8],"with":[9,35],"interpretability,":[10],"particularly":[11],"when":[12],"handling":[13],"highly":[14],"volatile":[15],"and":[16,61,96,109,132,135,141,190,197],"uncertain":[17],"data.":[18,76],"This":[19],"study":[20],"introduces":[21],"a":[22,86,112,137,195],"novel":[23],"interpretable":[24],"framework":[26,199],"integrates":[28],"the":[29,36,50,62,66,72,97,145,169],"Fuzzy":[30,38],"Time":[31],"Series":[32],"(FTS)":[33],"model":[34,45,171],"Linear":[37],"Information":[39],"Granule":[40],"(LFIG)":[41],"method.":[42],"The":[43,77],"proposed":[44,170],"addresses":[46],"two":[47],"major":[48],"limitations:":[49],"inability":[51],"of":[52,65,74,80,99,139,179],"conventional":[53],"FTS":[54,140],"to":[56,69,128,154],"effectively":[57],"capture":[58],"trend":[59,113,192],"dynamics,":[60],"limited":[63],"capacity":[64],"LFIG":[67,118,142],"method":[68],"account":[70],"for":[71,200],"influence":[73],"recent":[75],"core":[78],"contributions":[79],"this":[81],"work":[82],"are":[83],"threefold:":[84],"(1)":[85],"variable-sized":[87],"interval":[88],"partitioning":[89],"technique":[90],"optimized":[91],"via":[92,144],"fuzzy":[93,188],"C-means":[94],"clustering":[95],"principle":[98],"justifiable":[100],"granularity,":[101],"achieving":[102],"adaptive":[103],"data":[104],"segmentation":[105],"balances":[107],"coverage":[108],"specificity;":[110],"(2)":[111],"extraction":[114],"mechanism":[115],"based":[116],"on":[117,160],"approach,":[119],"which":[120,150],"applies":[121],"time-dependent":[122],"linear":[123],"functions":[124],"within":[125],"sliding":[126],"windows":[127],"quantify":[129],"short-term":[130],"trends":[131],"associated":[133],"uncertainties;":[134],"(3)":[136],"fusion":[138],"outputs":[143],"ordered":[146],"weighted":[147],"averaging":[148],"operator,":[149],"emphasizes":[151],"trend-consistent":[152],"predictions":[153],"enhance":[155],"accuracy.":[157],"Experimental":[158],"evaluation":[159],"five":[161],"benchmark":[162],"datasets":[163],"from":[164],"Yahoo":[165],"Finance":[166],"demonstrates":[167],"outperforms":[172],"eight":[173],"state-of-the-art":[174],"methods":[176],"in":[177],"terms":[178],"predictive":[180],"performance.":[181],"Furthermore,":[182],"it":[183],"maintains":[184],"interpretability":[185],"through":[186],"transparent":[187],"rules":[189],"explicit":[191],"representations,":[193],"providing":[194],"robust":[196],"explainable":[198],"stock":[201],"forecasting.":[203]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":1}],"updated_date":"2026-05-07T13:39:58.223016","created_date":"2025-10-10T00:00:00"}