{"id":"https://openalex.org/W4321021241","doi":"https://doi.org/10.1109/access.2023.3244599","title":"Integrating Piecewise Linear Representation and Deep Learning for Trading Signals Forecasting","display_name":"Integrating Piecewise Linear Representation and Deep Learning for Trading Signals Forecasting","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4321021241","doi":"https://doi.org/10.1109/access.2023.3244599"},"language":"en","primary_location":{"id":"doi:10.1109/access.2023.3244599","is_oa":true,"landing_page_url":"http://dx.doi.org/10.1109/access.2023.3244599","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10043675.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10043675.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100610235","display_name":"Yingjun Chen","orcid":"https://orcid.org/0000-0001-7121-862X"},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yingjun Chen","raw_affiliation_strings":["Department of Computer Science and Technology, Tongji University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Technology, Tongji University, Shanghai, China","institution_ids":["https://openalex.org/I116953780"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5008774513","display_name":"Zhigang Zhu","orcid":"https://orcid.org/0000-0002-0911-179X"},"institutions":[{"id":"https://openalex.org/I148128674","display_name":"University of Shanghai for Science and Technology","ror":"https://ror.org/00ay9v204","country_code":"CN","type":"education","lineage":["https://openalex.org/I148128674"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhigang Zhu","raw_affiliation_strings":["School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China","institution_ids":["https://openalex.org/I148128674"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5100610235"],"corresponding_institution_ids":["https://openalex.org/I116953780"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.7563,"has_fulltext":true,"cited_by_count":3,"citation_normalized_percentile":{"value":0.72653096,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":97},"biblio":{"volume":"11","issue":null,"first_page":"15184","last_page":"15197"},"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/T14319","display_name":"Currency Recognition and Detection","score":0.9940999746322632,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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":0.989799976348877,"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/computer-science","display_name":"Computer science","score":0.7578209638595581},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5723236799240112},{"id":"https://openalex.org/keywords/profitability-index","display_name":"Profitability index","score":0.5528643727302551},{"id":"https://openalex.org/keywords/volatility","display_name":"Volatility (finance)","score":0.5421044230461121},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5237584114074707},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.47955748438835144},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4710312485694885},{"id":"https://openalex.org/keywords/trading-strategy","display_name":"Trading strategy","score":0.47060641646385193},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.44302356243133545},{"id":"https://openalex.org/keywords/algorithmic-trading","display_name":"Algorithmic trading","score":0.43600964546203613},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4308394193649292},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.41060835123062134},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3420839309692383},{"id":"https://openalex.org/keywords/finance","display_name":"Finance","score":0.3091738224029541},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.09645149111747742}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7578209638595581},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5723236799240112},{"id":"https://openalex.org/C129361004","wikidata":"https://www.wikidata.org/wiki/Q2470236","display_name":"Profitability index","level":2,"score":0.5528643727302551},{"id":"https://openalex.org/C91602232","wikidata":"https://www.wikidata.org/wiki/Q756115","display_name":"Volatility (finance)","level":2,"score":0.5421044230461121},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5237584114074707},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.47955748438835144},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4710312485694885},{"id":"https://openalex.org/C131562839","wikidata":"https://www.wikidata.org/wiki/Q1574928","display_name":"Trading strategy","level":2,"score":0.47060641646385193},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.44302356243133545},{"id":"https://openalex.org/C78508483","wikidata":"https://www.wikidata.org/wiki/Q139445","display_name":"Algorithmic trading","level":2,"score":0.43600964546203613},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4308394193649292},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.41060835123062134},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3420839309692383},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.3091738224029541},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.09645149111747742},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2023.3244599","is_oa":true,"landing_page_url":"http://dx.doi.org/10.1109/access.2023.3244599","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10043675.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:c304065e7acb4dfdb5ed33632000f5d3","is_oa":true,"landing_page_url":"https://doaj.org/article/c304065e7acb4dfdb5ed33632000f5d3","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":"IEEE Access, Vol 11, Pp 15184-15197 (2023)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2023.3244599","is_oa":true,"landing_page_url":"http://dx.doi.org/10.1109/access.2023.3244599","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10043675.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4321021241.pdf","grobid_xml":"https://content.openalex.org/works/W4321021241.grobid-xml"},"referenced_works_count":68,"referenced_works":["https://openalex.org/W367306465","https://openalex.org/W1536929369","https://openalex.org/W1980850818","https://openalex.org/W1984500452","https://openalex.org/W2016053056","https://openalex.org/W2045001586","https://openalex.org/W2052466350","https://openalex.org/W2059047370","https://openalex.org/W2064675550","https://openalex.org/W2094042860","https://openalex.org/W2137359593","https://openalex.org/W2147800946","https://openalex.org/W2160815625","https://openalex.org/W2194775991","https://openalex.org/W2217047246","https://openalex.org/W2252215182","https://openalex.org/W2260992041","https://openalex.org/W2265846598","https://openalex.org/W2302255633","https://openalex.org/W2518055200","https://openalex.org/W2525614189","https://openalex.org/W2572554044","https://openalex.org/W2586356223","https://openalex.org/W2593555562","https://openalex.org/W2593842564","https://openalex.org/W2601029648","https://openalex.org/W2618530766","https://openalex.org/W2758567761","https://openalex.org/W2768552067","https://openalex.org/W2771885314","https://openalex.org/W2772202816","https://openalex.org/W2772285495","https://openalex.org/W2800569739","https://openalex.org/W2807137789","https://openalex.org/W2919115771","https://openalex.org/W2947411064","https://openalex.org/W3003986983","https://openalex.org/W3007346690","https://openalex.org/W3014974815","https://openalex.org/W3082557311","https://openalex.org/W3097382998","https://openalex.org/W3112701542","https://openalex.org/W3120831821","https://openalex.org/W3121251852","https://openalex.org/W3122544750","https://openalex.org/W3123982987","https://openalex.org/W3131724164","https://openalex.org/W3136021864","https://openalex.org/W3170147864","https://openalex.org/W3175078121","https://openalex.org/W3190730109","https://openalex.org/W3217705725","https://openalex.org/W4206990000","https://openalex.org/W4212821967","https://openalex.org/W4220696253","https://openalex.org/W4225320732","https://openalex.org/W4285025082","https://openalex.org/W4285541979","https://openalex.org/W4289744562","https://openalex.org/W4292168395","https://openalex.org/W6632100814","https://openalex.org/W6639568328","https://openalex.org/W6682864246","https://openalex.org/W6688752199","https://openalex.org/W6717177883","https://openalex.org/W6745526139","https://openalex.org/W6770386704","https://openalex.org/W6910546390"],"related_works":["https://openalex.org/W4313491999","https://openalex.org/W3195402399","https://openalex.org/W2523594540","https://openalex.org/W4206190611","https://openalex.org/W4231485458","https://openalex.org/W4386992034","https://openalex.org/W3213042429","https://openalex.org/W2503079957","https://openalex.org/W4323268808","https://openalex.org/W4311009023"],"abstract_inverted_index":{"Trading":[0],"signals":[1,45],"forecasting":[2,37],"is":[3,20,124],"an":[4],"interesting":[5],"but":[6],"challenging":[7],"research":[8],"topic":[9],"in":[10,36],"the":[11,17,66,89,99,128,132,136,165],"field":[12],"of":[13,78,146,155,167],"financial":[14,18,67,137],"investment,":[15],"since":[16],"market":[19],"a":[21,33,50,60,76,94,102,112,144],"nonlinearity":[22],"and":[23,32,87,111,135,151,158,183],"high":[24],"volatility":[25],"system":[26],"influenced":[27],"by":[28],"too":[29],"many":[30],"factors,":[31],"small":[34],"improvement":[35],"performance":[38,166,182],"can":[39],"bring":[40],"profits.":[41],"To":[42],"realize":[43],"trading":[44,68,85,90,133],"detection,":[46],"this":[47],"paper":[48],"presents":[49],"novel":[51],"method":[52,178],"which":[53],"integrates":[54],"piecewise":[55],"linear":[56],"representation":[57],"(PLR)":[58],"with":[59,185],"deep":[61],"learning":[62],"framework":[63,100],"to":[64,74,126,163],"predict":[65],"points.":[69],"Firstly,":[70],"we":[71,142],"utilize":[72],"PLR":[73],"generate":[75],"number":[77],"turning":[79],"points":[80,91,134],"(valleys":[81],"or":[82],"peaks)":[83],"from":[84],"data":[86],"formulate":[88],"prediction":[92,129],"as":[93],"three-class":[95],"classification":[96],"problem.":[97],"Then,":[98],"combined":[101],"convolutional":[103],"neural":[104],"network":[105,117],"(CNN)":[106],"for":[107,118],"spatial":[108],"features":[109,121],"extraction":[110,122],"long":[113],"short-term":[114],"memory":[115],"(LSTM)":[116],"temporal":[119],"domain":[120],"(CNN-LSTM)":[123],"used":[125],"learn":[127],"model":[130,181],"between":[131],"time":[138],"series":[139,145],"data.":[140],"Finally,":[141],"conduct":[143],"experiments":[147],"among":[148],"PLR-CNN-LSTM,":[149],"PLR-CNN-TA":[150],"PLR-LSTM":[152],"on":[153],"companies":[154],"US,":[156],"Turkey":[157],"daily":[159],"Exchange-Traded":[160],"Fund":[161],"(ETFs)":[162],"test":[164],"our":[168,176],"established":[169],"method.":[170],"The":[171],"experiment":[172],"results":[173],"show":[174],"that":[175],"proposed":[177],"has":[179],"better":[180],"profitability":[184],"different":[186],"investment":[187],"strategies.":[188]},"counts_by_year":[{"year":2024,"cited_by_count":3}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}