{"id":"https://openalex.org/W2767085244","doi":"https://doi.org/10.1109/ssci.2017.8280883","title":"Combining the real-time wavelet denoising and long-short-term-memory neural network for predicting stock indexes","display_name":"Combining the real-time wavelet denoising and long-short-term-memory neural network for predicting stock indexes","publication_year":2017,"publication_date":"2017-11-01","ids":{"openalex":"https://openalex.org/W2767085244","doi":"https://doi.org/10.1109/ssci.2017.8280883","mag":"2767085244"},"language":"en","primary_location":{"id":"doi:10.1109/ssci.2017.8280883","doi":"https://doi.org/10.1109/ssci.2017.8280883","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ssci.2017.8280883","pdf_url":null,"source":{"id":"https://openalex.org/S4363604921","display_name":"IEEE Symposium Series on Computational Intelligence (SSCI)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE Symposium Series on Computational Intelligence (SSCI)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"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/A5101764801","display_name":"Zhixi Li","orcid":"https://orcid.org/0000-0003-2425-6820","observed_orcids":["https://orcid.org/0000-0003-2425-6820"]},"institutions":[{"id":"https://openalex.org/I889458895","display_name":"University of Hong Kong","ror":"https://ror.org/02zhqgq86","country_code":"HK","type":"education","lineage":["https://openalex.org/I889458895"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Zhixi Li","raw_affiliation_strings":["Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China","institution_ids":["https://openalex.org/I889458895"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5111997888","display_name":"Vincent W. L. Tam","orcid":"https://orcid.org/0000-0003-4697-8277","observed_orcids":["https://orcid.org/0000-0003-4697-8277"]},"institutions":[{"id":"https://openalex.org/I889458895","display_name":"University of Hong Kong","ror":"https://ror.org/02zhqgq86","country_code":"HK","type":"education","lineage":["https://openalex.org/I889458895"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Vincent Tam","raw_affiliation_strings":["Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China","institution_ids":["https://openalex.org/I889458895"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I889458895"],"apc_list":null,"apc_paid":null,"fwci":2.7142,"has_fulltext":false,"cited_by_count":36,"citation_normalized_percentile":{"value":0.91069347,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":88,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":1.0,"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":1.0,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.0,"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"}},{"id":"https://openalex.org/T11918","display_name":"Forecasting Techniques and Applications","score":0.0,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/stock-index-forecasting","display_name":"stock index forecasting","score":0.8420000076293945},{"id":"https://openalex.org/keywords/wavelet-denoising","display_name":"wavelet denoising","score":0.9700000286102295},{"id":"https://openalex.org/keywords/long-short-term-memory-networks","display_name":"long short-term memory networks","score":0.8009999990463257},{"id":"https://openalex.org/keywords/time-series-forecasting","display_name":"time series forecasting","score":0.7279999852180481},{"id":"https://openalex.org/keywords/east-asian-stock-markets","display_name":"East Asian stock markets","score":0.8090000152587891},{"id":"https://openalex.org/keywords/recurrent-neural-networks","display_name":"recurrent neural networks","score":0.7170000076293945}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7387169599533081},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.6543030738830566},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.645177960395813},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5911715626716614},{"id":"https://openalex.org/C88389905","wikidata":"https://www.wikidata.org/wiki/Q223371","display_name":"Stock market index","level":4,"score":0.5537070035934448},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5305001735687256},{"id":"https://openalex.org/C2780299701","wikidata":"https://www.wikidata.org/wiki/Q475000","display_name":"Stock market","level":3,"score":0.48799529671669006},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.4717859923839569},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.4299112558364868},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39478665590286255},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3890538513660431},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3869287967681885},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","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}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/ssci.2017.8280883","doi":"https://doi.org/10.1109/ssci.2017.8280883","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ssci.2017.8280883","pdf_url":null,"source":{"id":"https://openalex.org/S4363604921","display_name":"IEEE Symposium Series on Computational Intelligence (SSCI)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE Symposium Series on Computational Intelligence (SSCI)","raw_type":"proceedings-article"},{"id":"pmh:oai:hub.hku.hk:10722/248645","doi":null,"is_oa":false,"landing_page_url":"http://hdl.handle.net/10722/248645","pdf_url":null,"source":{"id":"https://openalex.org/S4377196271","display_name":"The HKU Scholars Hub (University of Hong Kong)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I889458895","host_organization_name":"University of Hong Kong","host_organization_lineage":["https://openalex.org/I889458895"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Conference_Paper"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W1489213177","https://openalex.org/W1560739766","https://openalex.org/W1998244781","https://openalex.org/W2040940879","https://openalex.org/W2077447589","https://openalex.org/W2086694651","https://openalex.org/W2125527601","https://openalex.org/W2132984323","https://openalex.org/W2143612262","https://openalex.org/W2160725657","https://openalex.org/W2164392650","https://openalex.org/W2236966765","https://openalex.org/W2292977547","https://openalex.org/W2325606010","https://openalex.org/W2402268235","https://openalex.org/W2501684881","https://openalex.org/W2594919904","https://openalex.org/W2891692146","https://openalex.org/W4234667859","https://openalex.org/W4285719527"],"related_works":["https://openalex.org/W4386921443","https://openalex.org/W2021938316","https://openalex.org/W3155820657","https://openalex.org/W3088858312","https://openalex.org/W2022365299","https://openalex.org/W3159338921","https://openalex.org/W4384565632","https://openalex.org/W2997421965","https://openalex.org/W2956992481","https://openalex.org/W2542188015"],"abstract_inverted_index":{"A":[0],"stock":[1,15,102,138,151,225],"market":[2,16],"index":[3,35,104],"can":[4],"be":[5,190],"a":[6,14,18,39,49,70,144,208,233],"valuable":[7],"indicator":[8],"to":[9,26,52,112,148,159,192,211,220,237,271],"describe":[10],"the":[11,34,45,54,64,84,99,130,137,163,173,199,218,222,229,239,259,274,280,293,301],"performance":[12,260],"of":[13,42,77,86,101,198,261,273,304],"in":[17,58,83,153,162,168,182,196,205,227],"particular":[19],"region.":[20],"Nevertheless,":[21,89],"it":[22],"is":[23,48,69,246],"very":[24],"difficult":[25],"forecast":[27],"its":[28,154,243],"future":[29,150,240,302],"values":[30],"or":[31,103],"trends":[32],"since":[33],"data":[36,152,241],"often":[37],"demonstrate":[38],"high":[40],"degree":[41],"fluctuations.":[43],"Intrinsically,":[44],"wavelet":[46,114,133,174,214,230,281],"denoising":[47,115,136,215,231,282],"useful":[50],"method":[51],"separate":[53],"signals":[55],"from":[56,123],"noise":[57],"many":[59,287],"practical":[60],"multi-media":[61],"applications":[62],"while":[63,242],"long-short-term-memory":[65],"neural":[66,119],"network":[67,74,120],"(LSTM)":[68],"powerful":[71],"recurrent":[72],"neutral":[73],"(RNN)":[75],"architecture":[76],"learning":[78,298],"and":[79,176,289],"prediction":[80,100,200,264],"models":[81],"used":[82],"field":[85],"computational":[87],"intelligence.":[88],"few":[90],"research":[91],"studies":[92,184],"have":[93],"ever":[94],"considered":[95],"their":[96],"combination":[97],"for":[98,135,185,300],"movement.":[105],"More":[106],"importantly,":[107],"some":[108,251],"existing":[109],"proposals":[110],"trying":[111],"combine":[113,212],"with":[116,217,295],"other":[117,296],"artificial":[118],"architectures":[121],"suffer":[122],"two":[124],"major":[125],"drawbacks.":[126],"First,":[127],"when":[128,269],"applying":[129],"conventional":[131],"one-time":[132],"transform":[134],"data,":[139],"this":[140,206,305],"approach":[141],"has":[142],"made":[143],"serious":[145],"logical":[146],"flaw":[147],"include":[149],"training":[155],"phase,":[156],"thus":[157],"leading":[158],"impressive":[160],"results":[161,195,256],"backtesting":[164],"yet":[165],"actually":[166],"impractical":[167],"real-world":[169],"applications.":[170],"In":[171],"addition,":[172],"functions":[175,216],"decomposition":[177],"levels":[178],"are":[179,286],"typically":[180],"fixed":[181],"those":[183,272],"which":[186,228],"they":[187],"will":[188],"not":[189],"able":[191],"produce":[193],"optimal":[194],"terms":[197],"accuracies.":[201],"Hence,":[202],"we":[203],"propose":[204],"paper":[207],"novel":[209],"model":[210,265,277],"real-time":[213],"LSTM":[219,276],"predict":[221],"East":[223],"Asian":[224],"indexes":[226],"adopts":[232],"sliding":[234],"window":[235],"mechanism":[236],"exclude":[238],"system":[244],"configuration":[245],"flexibly":[247],"optimized":[248],"based":[249],"on":[250],"predefined":[252],"criteria.":[253],"The":[254],"empirical":[255],"reveal":[257],"that":[258],"our":[262],"proposed":[263],"shows":[266],"significant":[267],"improvements":[268],"compared":[270],"original":[275],"without":[278],"utilizing":[279],"function.":[283],"Furthermore,":[284],"there":[285],"interesting":[288],"possible":[290],"directions":[291],"including":[292],"integration":[294],"deep":[297],"networks":[299],"investigation":[303],"work.":[306]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":7},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":11},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":1}],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2017-11-10T00:00:00"}
