{"id":"https://openalex.org/W2787361075","doi":"https://doi.org/10.1109/sii.2017.8279187","title":"FEPA \u2014 An integrated computational intelligence model for predicting financial time series","display_name":"FEPA \u2014 An integrated computational intelligence model for predicting financial time series","publication_year":2017,"publication_date":"2017-12-01","ids":{"openalex":"https://openalex.org/W2787361075","doi":"https://doi.org/10.1109/sii.2017.8279187","mag":"2787361075"},"language":"en","primary_location":{"id":"doi:10.1109/sii.2017.8279187","is_oa":false,"landing_page_url":"https://doi.org/10.1109/sii.2017.8279187","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE/SICE International Symposium on System Integration (SII)","raw_type":"proceedings-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/A5007222628","display_name":"Heping Pan","orcid":"https://orcid.org/0000-0003-3897-9798"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Heping Pan","raw_affiliation_strings":["Intelligent Finance Research Center, Swingtum Prediction, Chongqing, Australia"],"affiliations":[{"raw_affiliation_string":"Intelligent Finance Research Center, Swingtum Prediction, Chongqing, Australia","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101944011","display_name":"Yu Ma","orcid":"https://orcid.org/0000-0002-3462-0748"},"institutions":[{"id":"https://openalex.org/I146563203","display_name":"University of International Business and Economics","ror":"https://ror.org/05khqpb71","country_code":"CN","type":"education","lineage":["https://openalex.org/I146563203"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yu Ma","raw_affiliation_strings":["School of Banking & Finance, University of International Business & Economics, Beijing"],"affiliations":[{"raw_affiliation_string":"School of Banking & Finance, University of International Business & Economics, Beijing","institution_ids":["https://openalex.org/I146563203"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5032732257","display_name":"Chengzhao Zhang","orcid":"https://orcid.org/0000-0002-3039-3453"},"institutions":[{"id":"https://openalex.org/I207528943","display_name":"Chengdu Medical College","ror":"https://ror.org/01c4jmp52","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I207528943"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chengzhao Zhang","raw_affiliation_strings":["Chengdu Politechnic, Chengdu"],"affiliations":[{"raw_affiliation_string":"Chengdu Politechnic, Chengdu","institution_ids":["https://openalex.org/I207528943"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5007222628"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.2583,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.65861219,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"34","issue":null,"first_page":"47","last_page":"52"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.996999979019165,"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":0.996999979019165,"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/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.9868999719619751,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/T11052","display_name":"Energy Load and Power Forecasting","score":0.9835000038146973,"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/sliding-window-protocol","display_name":"Sliding window protocol","score":0.7370243668556213},{"id":"https://openalex.org/keywords/hilbert\u2013huang-transform","display_name":"Hilbert\u2013Huang transform","score":0.6744095087051392},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.650446891784668},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.6127392649650574},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6018739938735962},{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"Principal component analysis","score":0.560589075088501},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.53023362159729},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5128970146179199},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.5055811405181885},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4435620605945587},{"id":"https://openalex.org/keywords/dimension","display_name":"Dimension (graph theory)","score":0.42698949575424194},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.420302152633667},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3636625409126282},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3315609395503998},{"id":"https://openalex.org/keywords/window","display_name":"Window (computing)","score":0.15734827518463135},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.15534105896949768}],"concepts":[{"id":"https://openalex.org/C102392041","wikidata":"https://www.wikidata.org/wiki/Q592860","display_name":"Sliding window protocol","level":3,"score":0.7370243668556213},{"id":"https://openalex.org/C25570617","wikidata":"https://www.wikidata.org/wiki/Q1006462","display_name":"Hilbert\u2013Huang transform","level":3,"score":0.6744095087051392},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.650446891784668},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.6127392649650574},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6018739938735962},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.560589075088501},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.53023362159729},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5128970146179199},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.5055811405181885},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4435620605945587},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.42698949575424194},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.420302152633667},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3636625409126282},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3315609395503998},{"id":"https://openalex.org/C2778751112","wikidata":"https://www.wikidata.org/wiki/Q835016","display_name":"Window (computing)","level":2,"score":0.15734827518463135},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15534105896949768},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/sii.2017.8279187","is_oa":false,"landing_page_url":"https://doi.org/10.1109/sii.2017.8279187","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE/SICE International Symposium on System Integration (SII)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Decent work and economic growth","score":0.49000000953674316,"id":"https://metadata.un.org/sdg/8"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W1276491955","https://openalex.org/W1966935273","https://openalex.org/W1979575715","https://openalex.org/W1986478348","https://openalex.org/W1999996900","https://openalex.org/W2007221293","https://openalex.org/W2034099719","https://openalex.org/W2075243656","https://openalex.org/W2093311990","https://openalex.org/W3015379812","https://openalex.org/W6628101947","https://openalex.org/W7024896121"],"related_works":["https://openalex.org/W1995622179","https://openalex.org/W1484111231","https://openalex.org/W1552543208","https://openalex.org/W2074396517","https://openalex.org/W2166963679","https://openalex.org/W2187269125","https://openalex.org/W1641615907","https://openalex.org/W3089231081","https://openalex.org/W2093956241","https://openalex.org/W2354420595"],"abstract_inverted_index":{"This":[0,95],"paper":[1],"presents":[2],"an":[3,89],"integrated":[4],"computational":[5],"intelligence":[6],"model":[7,44,140],"-":[8,10,151],"FEPA":[9],"for":[11,22,29,40,113],"predicting":[12],"financial":[13],"time":[14,54,131],"series":[15,55],"(FTS),":[16],"integrating":[17],"Empirical":[18],"Mode":[19,66],"Decomposition":[20],"(EMD)":[21],"signal":[23],"processing,":[24],"Principal":[25],"Component":[26],"Analysis":[27],"(PCA)":[28],"dimension":[30],"reduction":[31],"and":[32,35,77,135,154,163,168,170],"feature":[33],"extraction,":[34],"Artificial":[36],"Neural":[37],"Networks":[38],"(ANN)":[39],"prediction":[41],"modeling.":[42],"The":[43,138],"uses":[45,69],"a":[46,80,103,110,158],"sliding":[47,111],"window":[48,112],"to":[49,59,71,78,91],"capture":[50],"the":[51,61,73,93,115,144],"most":[52],"recent":[53],"data,":[56],"applies":[57],"EMD":[58,107],"transform":[60],"data":[62,146],"into":[63,88],"multilevel":[64],"Intrinsic":[65],"Functions":[67],"(IMF's),":[68],"PCA":[70],"reduce":[72],"dimensionality":[74],"of":[75,82,106,118,122,130,147,161],"IMF's":[76],"generate":[79,92],"set":[81],"information-rich":[83],"features":[84],"which":[85],"are":[86],"input":[87],"ANN":[90],"prediction.":[94],"work":[96],"is":[97,141],"original":[98],"in":[99],"four":[100],"aspects:":[101],"1)":[102],"structural":[104],"reformulation":[105],"algorithm,":[108],"2)":[109],"tackling":[114],"end":[116],"effect":[117],"EMD,":[119],"3)":[120],"investigation":[121],"multi-step":[123],"prediction,":[124],"4)":[125],"testing":[126],"on":[127,143,165,172],"two":[128,148],"levels":[129],"frame:":[132],"D1":[133,167,174],"(daily)":[134],"M15":[136],"(15-minutely).":[137],"new":[139],"tested":[142],"historical":[145],"stock":[149],"indices":[150],"Chinese":[152],"HS300":[153,166],"Australian":[155],"AXJO,":[156],"achieving":[157],"hit":[159],"rate":[160],"78%":[162],"82%":[164],"M15,":[169],"74%":[171],"AXJO":[173],"respectively.":[175]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2018,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
