{"id":"https://openalex.org/W2800026370","doi":"https://doi.org/10.1109/icpr.2018.8545666","title":"Recurrent Neural Networks for Financial Time-Series Modelling","display_name":"Recurrent Neural Networks for Financial Time-Series Modelling","publication_year":2018,"publication_date":"2018-08-01","ids":{"openalex":"https://openalex.org/W2800026370","doi":"https://doi.org/10.1109/icpr.2018.8545666","mag":"2800026370"},"language":"en","primary_location":{"id":"doi:10.1109/icpr.2018.8545666","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr.2018.8545666","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 24th International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://durham-repository.worktribe.com/output/1134539","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5073537068","display_name":"Gavin Tsang","orcid":"https://orcid.org/0000-0002-2035-1452"},"institutions":[{"id":"https://openalex.org/I39586589","display_name":"Swansea University","ror":"https://ror.org/053fq8t95","country_code":"GB","type":"education","lineage":["https://openalex.org/I39586589"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Gavin Tsang","raw_affiliation_strings":["Computer Science Department, Swansea University, United Kingdom"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science Department, Swansea University, United Kingdom","institution_ids":["https://openalex.org/I39586589"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082317295","display_name":"Jingjing Deng","orcid":"https://orcid.org/0000-0001-9274-651X"},"institutions":[{"id":"https://openalex.org/I39586589","display_name":"Swansea University","ror":"https://ror.org/053fq8t95","country_code":"GB","type":"education","lineage":["https://openalex.org/I39586589"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Jingjing Deng","raw_affiliation_strings":["Computer Science Department, Swansea University, United Kingdom"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science Department, Swansea University, United Kingdom","institution_ids":["https://openalex.org/I39586589"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5084010930","display_name":"Xianghua Xie","orcid":"https://orcid.org/0000-0002-2701-8660"},"institutions":[{"id":"https://openalex.org/I39586589","display_name":"Swansea University","ror":"https://ror.org/053fq8t95","country_code":"GB","type":"education","lineage":["https://openalex.org/I39586589"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Xianghua Xie","raw_affiliation_strings":["Computer Science Department, Swansea University, United Kingdom"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science Department, Swansea University, United Kingdom","institution_ids":["https://openalex.org/I39586589"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I39586589"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":26,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"892","last_page":"897"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9954000115394592,"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/T11052","display_name":"Energy Load and Power Forecasting","score":0.9911999702453613,"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/stock-market-prediction","display_name":"Stock market prediction","score":0.6757190227508545},{"id":"https://openalex.org/keywords/profitability-index","display_name":"Profitability index","score":0.6578289270401001},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.6550009250640869},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6476195454597473},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6473554968833923},{"id":"https://openalex.org/keywords/stock-market","display_name":"Stock market","score":0.6001396179199219},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5510028004646301},{"id":"https://openalex.org/keywords/financial-market","display_name":"Financial market","score":0.5467393398284912},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.5211922526359558},{"id":"https://openalex.org/keywords/stock-market-index","display_name":"Stock market index","score":0.5006930828094482},{"id":"https://openalex.org/keywords/index","display_name":"Index (typography)","score":0.4938672184944153},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.47206833958625793},{"id":"https://openalex.org/keywords/long-short-term-memory","display_name":"Long short term memory","score":0.4600372910499573},{"id":"https://openalex.org/keywords/closing","display_name":"Closing (real estate)","score":0.44375932216644287},{"id":"https://openalex.org/keywords/market-data","display_name":"Market data","score":0.430858850479126},{"id":"https://openalex.org/keywords/finance","display_name":"Finance","score":0.42353811860084534},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.39355960488319397},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.3635363280773163},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.35083872079849243},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.16404849290847778}],"concepts":[{"id":"https://openalex.org/C2776256503","wikidata":"https://www.wikidata.org/wiki/Q7617906","display_name":"Stock market prediction","level":4,"score":0.6757190227508545},{"id":"https://openalex.org/C129361004","wikidata":"https://www.wikidata.org/wiki/Q2470236","display_name":"Profitability index","level":2,"score":0.6578289270401001},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.6550009250640869},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6476195454597473},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6473554968833923},{"id":"https://openalex.org/C2780299701","wikidata":"https://www.wikidata.org/wiki/Q475000","display_name":"Stock market","level":3,"score":0.6001396179199219},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5510028004646301},{"id":"https://openalex.org/C19244329","wikidata":"https://www.wikidata.org/wiki/Q208697","display_name":"Financial market","level":2,"score":0.5467393398284912},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.5211922526359558},{"id":"https://openalex.org/C88389905","wikidata":"https://www.wikidata.org/wiki/Q223371","display_name":"Stock market index","level":4,"score":0.5006930828094482},{"id":"https://openalex.org/C2777382242","wikidata":"https://www.wikidata.org/wiki/Q6017816","display_name":"Index (typography)","level":2,"score":0.4938672184944153},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.47206833958625793},{"id":"https://openalex.org/C133488467","wikidata":"https://www.wikidata.org/wiki/Q6673524","display_name":"Long short term memory","level":4,"score":0.4600372910499573},{"id":"https://openalex.org/C2778775528","wikidata":"https://www.wikidata.org/wiki/Q5135432","display_name":"Closing (real estate)","level":2,"score":0.44375932216644287},{"id":"https://openalex.org/C114118609","wikidata":"https://www.wikidata.org/wiki/Q3036837","display_name":"Market data","level":2,"score":0.430858850479126},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.42353811860084534},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.39355960488319397},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.3635363280773163},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35083872079849243},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.16404849290847778},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"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},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"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/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/icpr.2018.8545666","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr.2018.8545666","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 24th International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"},{"id":"pmh:oai:cronfa.swan.ac.uk:cronfa39477","is_oa":false,"landing_page_url":"https://cronfa.swan.ac.uk/Record/cronfa39477","pdf_url":null,"source":{"id":"https://openalex.org/S4306401612","display_name":"Cronfa (Swansea University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I39586589","host_organization_name":"Swansea University","host_organization_lineage":["https://openalex.org/I39586589"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":null},{"id":"pmh:oai:durham-repository.worktribe.com:1134539","is_oa":true,"landing_page_url":"https://durham-repository.worktribe.com/output/1134539","pdf_url":null,"source":{"id":"https://openalex.org/S4306400188","display_name":"Durham Research Online (Durham University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I190082696","host_organization_name":"Durham University","host_organization_lineage":["https://openalex.org/I190082696"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Presentation / Conference Contribution"}],"best_oa_location":{"id":"pmh:oai:durham-repository.worktribe.com:1134539","is_oa":true,"landing_page_url":"https://durham-repository.worktribe.com/output/1134539","pdf_url":null,"source":{"id":"https://openalex.org/S4306400188","display_name":"Durham Research Online (Durham University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I190082696","host_organization_name":"Durham University","host_organization_lineage":["https://openalex.org/I190082696"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Presentation / Conference Contribution"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G7640577213","display_name":null,"funder_award_id":"1819112","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"}],"funders":[{"id":"https://openalex.org/F4320334627","display_name":"Engineering and Physical Sciences Research Council","ror":"https://ror.org/0439y7842"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W181010784","https://openalex.org/W1522301498","https://openalex.org/W1575726819","https://openalex.org/W1689711448","https://openalex.org/W1955848941","https://openalex.org/W1995456904","https://openalex.org/W2079724595","https://openalex.org/W2110321237","https://openalex.org/W2110798204","https://openalex.org/W2469469599","https://openalex.org/W2598966758","https://openalex.org/W2734777338","https://openalex.org/W2767192847","https://openalex.org/W2769305693","https://openalex.org/W2774060148","https://openalex.org/W2919115771","https://openalex.org/W2964121744","https://openalex.org/W4240307802","https://openalex.org/W4255521522","https://openalex.org/W6607466794","https://openalex.org/W6631190155","https://openalex.org/W6634434869","https://openalex.org/W6676481782","https://openalex.org/W6719924153","https://openalex.org/W6735195716","https://openalex.org/W6745907381"],"related_works":["https://openalex.org/W2912153778","https://openalex.org/W4387163678","https://openalex.org/W4288108708","https://openalex.org/W2973430807","https://openalex.org/W4385280324","https://openalex.org/W2984436043","https://openalex.org/W4390245176","https://openalex.org/W2912831041","https://openalex.org/W2890685186","https://openalex.org/W3173606726"],"abstract_inverted_index":{"The":[0],"prediction":[1,105],"of":[2,16,24,52,89,106],"financial":[3,107],"time":[4],"series":[5],"data":[6,40],"is":[7],"a":[8,22,31],"challenging":[9],"task":[10],"due":[11],"to":[12,62,73,91],"the":[13,58,64,75],"unpredictable":[14],"behaviours":[15],"investors":[17],"that":[18],"are":[19],"influenced":[20],"by":[21],"multitude":[23],"factors.":[25],"In":[26],"this":[27],"paper,":[28],"we":[29],"present":[30],"novel":[32],"deep":[33],"Long":[34],"Short-Term":[35],"Memory":[36],"(LSTM)":[37],"based":[38],"time-series":[39,102],"modelling":[41,103],"for":[42],"use":[43],"in":[44,66,101],"stock":[45],"market":[46,54,68],"index":[47],"prediction.":[48],"A":[49],"dataset":[50],"comprised":[51],"six":[53],"indices":[55],"from":[56],"around":[57],"world":[59],"were":[60],"chosen":[61],"demonstrate":[63],"robustness":[65],"varying":[67],"conditions":[69],"with":[70],"an":[71,84],"aim":[72],"forecast":[74],"next":[76],"day":[77],"closing":[78],"price.":[79],"With":[80],"experimental":[81],"results":[82,100],"showing":[83],"average":[85],"annual":[86],"profitability":[87],"performance":[88],"up":[90],"200%,":[92],"our":[93],"method":[94],"demonstrates":[95],"its":[96],"feasibility":[97],"and":[98,104],"significant":[99],"markets.":[108]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":1}],"updated_date":"2026-07-23T08:03:31.855105","created_date":"2025-10-10T00:00:00"}