{"id":"https://openalex.org/W4416501127","doi":"https://doi.org/10.1007/s44163-025-00622-0","title":"F-LOAM: an efficient hybrid model for stock price prediction based on SVMD denoising","display_name":"F-LOAM: an efficient hybrid model for stock price prediction based on SVMD denoising","publication_year":2025,"publication_date":"2025-11-21","ids":{"openalex":"https://openalex.org/W4416501127","doi":"https://doi.org/10.1007/s44163-025-00622-0"},"language":"en","primary_location":{"id":"doi:10.1007/s44163-025-00622-0","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s44163-025-00622-0","pdf_url":"https://link.springer.com/content/pdf/10.1007/s44163-025-00622-0.pdf","source":{"id":"https://openalex.org/S4210220416","display_name":"Discover Artificial Intelligence","issn_l":"2731-0809","issn":["2731-0809"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319965","host_organization_name":"Springer Nature","host_organization_lineage":["https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Discover Artificial Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://link.springer.com/content/pdf/10.1007/s44163-025-00622-0.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5031877531","display_name":"Liefa Liao","orcid":"https://orcid.org/0000-0002-1805-6614"},"institutions":[{"id":"https://openalex.org/I4210163048","display_name":"Jiangxi College of Applied Technology","ror":"https://ror.org/05d5kcc69","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210163048"]},{"id":"https://openalex.org/I4510145","display_name":"Jiangxi University of Science and Technology","ror":"https://ror.org/03q0t9252","country_code":"CN","type":"education","lineage":["https://openalex.org/I4510145"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Liefa Liao","raw_affiliation_strings":["President\u2019s Office, Jiangxi Modern Vocational and Technical College, 338 Ziyang Avenue, Changdong University Park, Nanchang, 330095, Jiangxi, China","School of Information Engineering, Jiangxi University of Science and Technology, No. 1958, Kejia Avenue, Ganzhou, 341000, Jiangxi, China"],"affiliations":[{"raw_affiliation_string":"President\u2019s Office, Jiangxi Modern Vocational and Technical College, 338 Ziyang Avenue, Changdong University Park, Nanchang, 330095, Jiangxi, China","institution_ids":["https://openalex.org/I4210163048"]},{"raw_affiliation_string":"School of Information Engineering, Jiangxi University of Science and Technology, No. 1958, Kejia Avenue, Ganzhou, 341000, Jiangxi, China","institution_ids":["https://openalex.org/I4510145"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101698126","display_name":"Jingjing Lin","orcid":"https://orcid.org/0000-0001-5156-2693"},"institutions":[{"id":"https://openalex.org/I4510145","display_name":"Jiangxi University of Science and Technology","ror":"https://ror.org/03q0t9252","country_code":"CN","type":"education","lineage":["https://openalex.org/I4510145"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jingjing Lin","raw_affiliation_strings":["School of Information Engineering, Jiangxi University of Science and Technology, No. 1958, Kejia Avenue, Ganzhou, 341000, Jiangxi, China"],"affiliations":[{"raw_affiliation_string":"School of Information Engineering, Jiangxi University of Science and Technology, No. 1958, Kejia Avenue, Ganzhou, 341000, Jiangxi, China","institution_ids":["https://openalex.org/I4510145"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5031877531"],"corresponding_institution_ids":["https://openalex.org/I4210163048","https://openalex.org/I4510145"],"apc_list":{"value":990,"currency":"EUR","value_usd":1067},"apc_paid":{"value":990,"currency":"EUR","value_usd":1067},"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.40245641,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"5","issue":"1","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9323999881744385,"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":0.9323999881744385,"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/T11052","display_name":"Energy Load and Power Forecasting","score":0.015599999576807022,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.005200000014156103,"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/sharpe-ratio","display_name":"Sharpe ratio","score":0.4887000024318695},{"id":"https://openalex.org/keywords/hyperparameter","display_name":"Hyperparameter","score":0.45719999074935913},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.4544999897480011},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.4496000111103058},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.3815999925136566},{"id":"https://openalex.org/keywords/gradient-boosting","display_name":"Gradient boosting","score":0.36820000410079956},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.3490999937057495},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.33500000834465027},{"id":"https://openalex.org/keywords/investment-strategy","display_name":"Investment strategy","score":0.3314000070095062},{"id":"https://openalex.org/keywords/modal","display_name":"Modal","score":0.32760000228881836}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6640999913215637},{"id":"https://openalex.org/C139938925","wikidata":"https://www.wikidata.org/wiki/Q1501898","display_name":"Sharpe ratio","level":3,"score":0.4887000024318695},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.45719999074935913},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.4544999897480011},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4526999890804291},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.4496000111103058},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.40880000591278076},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.40799999237060547},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.3982999920845032},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.3815999925136566},{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.36820000410079956},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3490999937057495},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.33500000834465027},{"id":"https://openalex.org/C103144560","wikidata":"https://www.wikidata.org/wiki/Q2670999","display_name":"Investment strategy","level":3,"score":0.3314000070095062},{"id":"https://openalex.org/C71139939","wikidata":"https://www.wikidata.org/wiki/Q910194","display_name":"Modal","level":2,"score":0.32760000228881836},{"id":"https://openalex.org/C2780299701","wikidata":"https://www.wikidata.org/wiki/Q475000","display_name":"Stock market","level":3,"score":0.32429999113082886},{"id":"https://openalex.org/C24338571","wikidata":"https://www.wikidata.org/wiki/Q2566298","display_name":"Autoregressive integrated moving average","level":3,"score":0.3240000009536743},{"id":"https://openalex.org/C139819358","wikidata":"https://www.wikidata.org/wiki/Q462748","display_name":"Asset allocation","level":3,"score":0.3091999888420105},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.3077999949455261},{"id":"https://openalex.org/C74746147","wikidata":"https://www.wikidata.org/wiki/Q5324652","display_name":"EWMA chart","level":4,"score":0.30630001425743103},{"id":"https://openalex.org/C204036174","wikidata":"https://www.wikidata.org/wiki/Q909380","display_name":"Stock (firearms)","level":2,"score":0.3059999942779541},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.298799991607666},{"id":"https://openalex.org/C2164484","wikidata":"https://www.wikidata.org/wiki/Q5170150","display_name":"Core (optical fiber)","level":2,"score":0.2980000078678131},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.2971000075340271},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.2946000099182129},{"id":"https://openalex.org/C2780821815","wikidata":"https://www.wikidata.org/wiki/Q5340806","display_name":"Portfolio","level":2,"score":0.28940001130104065},{"id":"https://openalex.org/C10485038","wikidata":"https://www.wikidata.org/wiki/Q48996162","display_name":"Hyperparameter optimization","level":3,"score":0.2838999927043915},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2754000127315521},{"id":"https://openalex.org/C101601086","wikidata":"https://www.wikidata.org/wiki/Q3753228","display_name":"Rank correlation","level":2,"score":0.26899999380111694},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.2671000063419342},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.2655999958515167},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.26460000872612},{"id":"https://openalex.org/C19244329","wikidata":"https://www.wikidata.org/wiki/Q208697","display_name":"Financial market","level":2,"score":0.25859999656677246},{"id":"https://openalex.org/C159744936","wikidata":"https://www.wikidata.org/wiki/Q1126730","display_name":"Spearman's rank correlation coefficient","level":2,"score":0.2563999891281128},{"id":"https://openalex.org/C206729178","wikidata":"https://www.wikidata.org/wiki/Q2271896","display_name":"Scheduling (production processes)","level":2,"score":0.2524999976158142}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1007/s44163-025-00622-0","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s44163-025-00622-0","pdf_url":"https://link.springer.com/content/pdf/10.1007/s44163-025-00622-0.pdf","source":{"id":"https://openalex.org/S4210220416","display_name":"Discover Artificial Intelligence","issn_l":"2731-0809","issn":["2731-0809"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319965","host_organization_name":"Springer Nature","host_organization_lineage":["https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Discover Artificial Intelligence","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:1e213efad087461294b4e04f55f9ea23","is_oa":true,"landing_page_url":"https://doaj.org/article/1e213efad087461294b4e04f55f9ea23","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Discover Artificial Intelligence, Vol 5, Iss 1, Pp 1-20 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1007/s44163-025-00622-0","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s44163-025-00622-0","pdf_url":"https://link.springer.com/content/pdf/10.1007/s44163-025-00622-0.pdf","source":{"id":"https://openalex.org/S4210220416","display_name":"Discover Artificial Intelligence","issn_l":"2731-0809","issn":["2731-0809"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319965","host_organization_name":"Springer Nature","host_organization_lineage":["https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Discover Artificial Intelligence","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G10184524","display_name":null,"funder_award_id":"71761018","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2087396116","display_name":null,"funder_award_id":"China","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3317480652","display_name":null,"funder_award_id":"Science","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4020255992","display_name":null,"funder_award_id":"Project","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5994120800","display_name":null,"funder_award_id":"Natural","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7203782485","display_name":null,"funder_award_id":"71462018","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":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4416501127.pdf","grobid_xml":"https://content.openalex.org/works/W4416501127.grobid-xml"},"referenced_works_count":48,"referenced_works":["https://openalex.org/W2000982976","https://openalex.org/W2041018175","https://openalex.org/W2064675550","https://openalex.org/W2791125525","https://openalex.org/W2949676527","https://openalex.org/W2999171153","https://openalex.org/W3082523044","https://openalex.org/W3130456109","https://openalex.org/W3137774199","https://openalex.org/W3188822819","https://openalex.org/W4212791338","https://openalex.org/W4213234689","https://openalex.org/W4220875904","https://openalex.org/W4224316292","https://openalex.org/W4292519133","https://openalex.org/W4306406940","https://openalex.org/W4382933677","https://openalex.org/W4386470331","https://openalex.org/W4388028818","https://openalex.org/W4389117490","https://openalex.org/W4390047499","https://openalex.org/W4390590544","https://openalex.org/W4390966069","https://openalex.org/W4396930351","https://openalex.org/W4399058679","https://openalex.org/W4399096407","https://openalex.org/W4399915348","https://openalex.org/W4401324765","https://openalex.org/W4402436873","https://openalex.org/W4405614070","https://openalex.org/W4405717919","https://openalex.org/W4408316425","https://openalex.org/W4409917097","https://openalex.org/W4409985161","https://openalex.org/W4410359635","https://openalex.org/W4410494620","https://openalex.org/W4410735827","https://openalex.org/W4410867132","https://openalex.org/W4411349426","https://openalex.org/W4411918558","https://openalex.org/W4411991012","https://openalex.org/W4412630602","https://openalex.org/W4412786703","https://openalex.org/W4412895527","https://openalex.org/W4413744088","https://openalex.org/W4413806526","https://openalex.org/W4414856445","https://openalex.org/W4415048970"],"related_works":[],"abstract_inverted_index":{"Stock":[0],"price":[1],"forecasting,":[2],"as":[3,68],"a":[4,56,104,188],"core":[5,62],"topic":[6],"in":[7,30,39,131],"financial":[8,31],"time":[9,122],"series":[10,123],"analysis,":[11],"is":[12],"crucial":[13],"for":[14,50,135,202],"enhancing":[15],"the":[16,34,44,82,97,144,151,167],"accuracy":[17],"of":[18,36,64,185,191],"quantitative":[19],"investment":[20,204],"decisions.":[21],"To":[22],"address":[23],"key":[24],"challenges":[25],"including":[26,158],"high":[27],"noise":[28],"levels":[29],"market":[32],"data,":[33],"limitations":[35],"single":[37],"models":[38],"capturing":[40],"temporal":[41],"features,":[42],"and":[43,155,160,176,187,197],"reliance":[45],"on":[46,150,178],"empirical":[47],"parameter":[48],"tuning":[49],"hyperparameter":[51],"optimization,":[52],"this":[53,65],"paper":[54],"proposes":[55],"Feature-enhanced":[57],"LSTM-Optuna-Augmented":[58],"Model":[59],"(F-LOAM).":[60],"The":[61],"innovations":[63],"study":[66],"are":[67,170],"follows:":[69],"(1)":[70],"proposing":[71],"an":[72,90,182,199],"enhanced":[73],"Variational":[74],"Modal":[75],"Decomposition":[76],"(VMD)":[77],"denoising":[78],"algorithm,":[79],"which":[80],"introduces":[81],"Spearman":[83],"Rank":[84],"Correlation":[85],"Coefficient":[86],"(SRCC)":[87],"to":[88],"establish":[89],"adaptive":[91],"modal":[92],"selection":[93],"mechanism,":[94],"thereby":[95],"improving":[96],"signal-to-noise":[98],"ratio":[99,190],"by":[100],"23.6%;":[101],"(2)":[102],"designing":[103],"cascaded":[105],"Long":[106],"Short-Term":[107],"Memory":[108],"(LSTM)-Light":[109],"Gradient":[110],"Boosting":[111],"Machine":[112],"(LightGBM)":[113],"architecture":[114],"that":[115],"first":[116],"extracts":[117],"high-order":[118],"feature":[119],"representations":[120],"from":[121],"data":[124,133],"using":[125],"LSTM,":[126],"then":[127],"utilizes":[128],"LightGBM\u2019s":[129],"strengths":[130],"large-scale":[132],"processing":[134],"precise":[136],"predictions,":[137],"while":[138],"optimizing":[139],"both":[140],"models\u2019":[141],"hyperparameters":[142],"through":[143],"Optuna":[145],"framework.":[146],"In":[147],"comprehensive":[148],"evaluations":[149],"self-built":[152],"MFD12":[153],"dataset":[154],"public":[156],"datasets":[157],"ACL18":[159],"KDD17,":[161],"F-LOAM":[162],"achieves":[163],"superior":[164],"performance.":[165],"Furthermore,":[166],"prediction":[168],"results":[169],"converted":[171],"into":[172],"executable":[173],"trading":[174],"strategies,":[175],"backtesting":[177],"live":[179],"markets":[180],"yield":[181],"annualised":[183],"return":[184],"20.27%":[186],"Sharpe":[189],"1.308,":[192],"demonstrating":[193],"outperforming":[194],"existing":[195],"methods":[196],"providing":[198],"innovative":[200],"solution":[201],"intelligent":[203],"decision-making.":[205]},"counts_by_year":[],"updated_date":"2026-04-15T08:11:43.952461","created_date":"2025-11-23T00:00:00"}
