{"id":"https://openalex.org/W4406020789","doi":"https://doi.org/10.1140/epjds/s13688-024-00517-7","title":"A hybrid stock prediction method based on periodic/non-periodic features analyses","display_name":"A hybrid stock prediction method based on periodic/non-periodic features analyses","publication_year":2025,"publication_date":"2025-01-03","ids":{"openalex":"https://openalex.org/W4406020789","doi":"https://doi.org/10.1140/epjds/s13688-024-00517-7"},"language":"en","primary_location":{"id":"doi:10.1140/epjds/s13688-024-00517-7","is_oa":true,"landing_page_url":"https://doi.org/10.1140/epjds/s13688-024-00517-7","pdf_url":"https://epjdatascience.springeropen.com/counter/pdf/10.1140/epjds/s13688-024-00517-7","source":{"id":"https://openalex.org/S2504380752","display_name":"EPJ Data Science","issn_l":"2193-1127","issn":["2193-1127"],"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":"EPJ Data Science","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://epjdatascience.springeropen.com/counter/pdf/10.1140/epjds/s13688-024-00517-7","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5108947978","display_name":"Cheng Zhao","orcid":"https://orcid.org/0009-0000-0197-3646"},"institutions":[{"id":"https://openalex.org/I55712492","display_name":"Zhejiang University of Technology","ror":"https://ror.org/02djqfd08","country_code":"CN","type":"education","lineage":["https://openalex.org/I55712492"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Cheng Zhao","raw_affiliation_strings":["School of Economics, Zhejiang University of Technology, Hangzhou, China"],"raw_orcid":"https://orcid.org/0009-0002-9655-6160","affiliations":[{"raw_affiliation_string":"School of Economics, Zhejiang University of Technology, Hangzhou, China","institution_ids":["https://openalex.org/I55712492"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113949660","display_name":"Junyi Cai","orcid":null},"institutions":[{"id":"https://openalex.org/I55712492","display_name":"Zhejiang University of Technology","ror":"https://ror.org/02djqfd08","country_code":"CN","type":"education","lineage":["https://openalex.org/I55712492"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junyi Cai","raw_affiliation_strings":["School of Computer Science, Zhejiang University of Technology, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science, Zhejiang University of Technology, Hangzhou, China","institution_ids":["https://openalex.org/I55712492"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101842133","display_name":"Shuyi Yang","orcid":"https://orcid.org/0000-0003-2019-4773"},"institutions":[{"id":"https://openalex.org/I55712492","display_name":"Zhejiang University of Technology","ror":"https://ror.org/02djqfd08","country_code":"CN","type":"education","lineage":["https://openalex.org/I55712492"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuyi Yang","raw_affiliation_strings":["School of Computer Science, Zhejiang University of Technology, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science, Zhejiang University of Technology, Hangzhou, China","institution_ids":["https://openalex.org/I55712492"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5108947978"],"corresponding_institution_ids":["https://openalex.org/I55712492"],"apc_list":{"value":1190,"currency":"GBP","value_usd":1459},"apc_paid":{"value":1190,"currency":"GBP","value_usd":1459},"fwci":21.4551,"has_fulltext":true,"cited_by_count":12,"citation_normalized_percentile":{"value":0.99352582,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":"14","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":0.9997000098228455,"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":0.9997000098228455,"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/T11918","display_name":"Forecasting Techniques and Applications","score":0.9937000274658203,"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":0.9912999868392944,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5344732403755188},{"id":"https://openalex.org/keywords/statistical-physics","display_name":"Statistical physics","score":0.47143474221229553},{"id":"https://openalex.org/keywords/stock","display_name":"Stock (firearms)","score":0.4313776195049286},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3704391121864319},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.15591564774513245},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.089179128408432}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5344732403755188},{"id":"https://openalex.org/C121864883","wikidata":"https://www.wikidata.org/wiki/Q677916","display_name":"Statistical physics","level":1,"score":0.47143474221229553},{"id":"https://openalex.org/C204036174","wikidata":"https://www.wikidata.org/wiki/Q909380","display_name":"Stock (firearms)","level":2,"score":0.4313776195049286},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3704391121864319},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.15591564774513245},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.089179128408432},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1140/epjds/s13688-024-00517-7","is_oa":true,"landing_page_url":"https://doi.org/10.1140/epjds/s13688-024-00517-7","pdf_url":"https://epjdatascience.springeropen.com/counter/pdf/10.1140/epjds/s13688-024-00517-7","source":{"id":"https://openalex.org/S2504380752","display_name":"EPJ Data Science","issn_l":"2193-1127","issn":["2193-1127"],"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":"EPJ Data Science","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:e009e8a93953450995688a05e9e358da","is_oa":true,"landing_page_url":"https://doaj.org/article/e009e8a93953450995688a05e9e358da","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":"EPJ Data Science, Vol 14, Iss 1, Pp 1-18 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1140/epjds/s13688-024-00517-7","is_oa":true,"landing_page_url":"https://doi.org/10.1140/epjds/s13688-024-00517-7","pdf_url":"https://epjdatascience.springeropen.com/counter/pdf/10.1140/epjds/s13688-024-00517-7","source":{"id":"https://openalex.org/S2504380752","display_name":"EPJ Data Science","issn_l":"2193-1127","issn":["2193-1127"],"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":"EPJ Data Science","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.4099999964237213,"id":"https://metadata.un.org/sdg/13","display_name":"Climate action"}],"awards":[{"id":"https://openalex.org/G1494935555","display_name":null,"funder_award_id":"61902349","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/W4406020789.pdf","grobid_xml":"https://content.openalex.org/works/W4406020789.grobid-xml"},"referenced_works_count":35,"referenced_works":["https://openalex.org/W1497414364","https://openalex.org/W1542765998","https://openalex.org/W1968378945","https://openalex.org/W1980831577","https://openalex.org/W1984315581","https://openalex.org/W2007221293","https://openalex.org/W2007821492","https://openalex.org/W2012079387","https://openalex.org/W2125056386","https://openalex.org/W2596922372","https://openalex.org/W2774513877","https://openalex.org/W2893815961","https://openalex.org/W2900880305","https://openalex.org/W2919115771","https://openalex.org/W2922995703","https://openalex.org/W2943143827","https://openalex.org/W2963751193","https://openalex.org/W2973403233","https://openalex.org/W2995189841","https://openalex.org/W3007075806","https://openalex.org/W3021318637","https://openalex.org/W3105750503","https://openalex.org/W3107642158","https://openalex.org/W3110378470","https://openalex.org/W3126284633","https://openalex.org/W3130456109","https://openalex.org/W4205627296","https://openalex.org/W4229446061","https://openalex.org/W4241584625","https://openalex.org/W4285402398","https://openalex.org/W4306320857","https://openalex.org/W4363650660","https://openalex.org/W4367397399","https://openalex.org/W6819738252","https://openalex.org/W6861937112"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052"],"abstract_inverted_index":{"Stock":[0,27],"investment":[1],"is":[2,21,30],"an":[3,174],"economic":[4],"activity":[5],"characterized":[6],"by":[7,173,184],"high":[8,11,39],"risks":[9],"and":[10,38,54,78,83,94,123,137,155,178,190],"returns.":[12],"Therefore,":[13],"the":[14,36,52,101,134,141,151,168,180,187],"prediction":[15,29,92],"of":[16,22,41,57,176],"stock":[17,42,58,88,102,148],"prices":[18,149],"or":[19],"fluctuations":[20],"great":[23],"importance":[24],"to":[25,35,80,129,146],"investors.":[26],"price":[28],"a":[31,66,115],"challenging":[32],"task":[33],"due":[34],"nonlinearity":[37],"volatility":[40],"time":[43,116,130],"series.":[44],"Existing":[45],"deep":[46],"learning":[47],"models":[48],"may":[49],"not":[50],"capture":[51,82],"periodic":[53,122],"non-periodic":[55,124],"features":[56],"data":[59,89,103],"effectively.":[60],"In":[61],"this":[62],"paper,":[63],"we":[64,95],"propose":[65],"novel":[67],"model":[68],"that":[69,119,165],"leverages":[70],"Complete":[71],"Ensemble":[72],"Empirical":[73],"Mode":[74],"Decomposition":[75],"(CEEMD),":[76],"Time2Vec,":[77],"Transformer":[79,132],"better":[81],"utilize":[84],"various":[85],"patterns":[86,125],"in":[87,150],"for":[90],"enhanced":[91],"performance,":[93],"call":[96],"it":[97,157],"ETT.":[98],"CEEMD":[99],"decomposes":[100],"into":[104],"different":[105],"frequency":[106],"components":[107],"based":[108],"on":[109,186],"their":[110],"intrinsic":[111],"scales.":[112],"Time2Vec":[113],"provides":[114],"vector":[117],"representation":[118],"captures":[120],"both":[121],"while":[126],"being":[127],"invariant":[128],"scaling.":[131],"learns":[133],"long-term":[135],"dependencies":[136],"global":[138],"information":[139],"from":[140],"data.":[142],"We":[143],"apply":[144],"ETT":[145,166],"predict":[147],"Chinese":[152],"A-share":[153],"market":[154],"compare":[156],"with":[158],"several":[159],"baseline":[160],"models.":[161],"The":[162],"results":[163],"show":[164],"reduces":[167],"mean":[169],"squared":[170],"error":[171],"(MSE)":[172],"average":[175,181],"4%":[177],"increases":[179],"cumulative":[182],"return":[183],"58%":[185],"CSI":[188],"100":[189],"Hushen":[191],"300":[192],"datasets.":[193]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":10}],"updated_date":"2026-05-07T13:39:58.223016","created_date":"2025-10-10T00:00:00"}
