{"id":"https://openalex.org/W3126256046","doi":"https://doi.org/10.1109/icoin50884.2021.9333853","title":"Bitcoin Price Forecasting via Ensemble-based LSTM Deep Learning Networks","display_name":"Bitcoin Price Forecasting via Ensemble-based LSTM Deep Learning Networks","publication_year":2021,"publication_date":"2021-01-13","ids":{"openalex":"https://openalex.org/W3126256046","doi":"https://doi.org/10.1109/icoin50884.2021.9333853","mag":"3126256046"},"language":"en","primary_location":{"id":"doi:10.1109/icoin50884.2021.9333853","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icoin50884.2021.9333853","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Conference on Information Networking (ICOIN)","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/A5002885336","display_name":"MyungJae Shin","orcid":"https://orcid.org/0000-0001-6505-1448"},"institutions":[{"id":"https://openalex.org/I2802835388","display_name":"Seoul National University Hospital","ror":"https://ror.org/01z4nnt86","country_code":"KR","type":"healthcare","lineage":["https://openalex.org/I139264467","https://openalex.org/I2802835388"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"MyungJae Shin","raw_affiliation_strings":["Seoul National University Hospital, Seoul, Republic of Korea"],"affiliations":[{"raw_affiliation_string":"Seoul National University Hospital, Seoul, Republic of Korea","institution_ids":["https://openalex.org/I2802835388"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077402873","display_name":"Aziz Mohaisen","orcid":"https://orcid.org/0000-0003-3227-2505"},"institutions":[{"id":"https://openalex.org/I106165777","display_name":"University of Central Florida","ror":"https://ror.org/036nfer12","country_code":"US","type":"education","lineage":["https://openalex.org/I106165777"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"David Mohaisen","raw_affiliation_strings":["University of Central Florida, Orlando, FL, USA"],"affiliations":[{"raw_affiliation_string":"University of Central Florida, Orlando, FL, USA","institution_ids":["https://openalex.org/I106165777"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5049202871","display_name":"Joongheon Kim","orcid":"https://orcid.org/0000-0003-2126-768X"},"institutions":[{"id":"https://openalex.org/I197347611","display_name":"Korea University","ror":"https://ror.org/047dqcg40","country_code":"KR","type":"education","lineage":["https://openalex.org/I197347611"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Joongheon Kim","raw_affiliation_strings":["Artificial Intelligence Engineering Research Center, College of Engineering, Korea University, Seoul, Republic of Korea","School of Electrical Engineering, Korea University, Seoul, Republic of Korea"],"affiliations":[{"raw_affiliation_string":"Artificial Intelligence Engineering Research Center, College of Engineering, Korea University, Seoul, Republic of Korea","institution_ids":["https://openalex.org/I197347611"]},{"raw_affiliation_string":"School of Electrical Engineering, Korea University, Seoul, Republic of Korea","institution_ids":["https://openalex.org/I197347611"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5002885336"],"corresponding_institution_ids":["https://openalex.org/I2802835388"],"apc_list":null,"apc_paid":null,"fwci":7.9869,"has_fulltext":false,"cited_by_count":32,"citation_normalized_percentile":{"value":0.97390198,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":93,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"603","last_page":"608"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10270","display_name":"Blockchain Technology Applications and Security","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10270","display_name":"Blockchain Technology Applications and Security","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T14319","display_name":"Currency Recognition and Detection","score":0.9957000017166138,"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/T11326","display_name":"Stock Market Forecasting Methods","score":0.9951000213623047,"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":0.7479928731918335},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6115260720252991},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5900416374206543},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5839521288871765},{"id":"https://openalex.org/keywords/ensemble-forecasting","display_name":"Ensemble forecasting","score":0.5780846476554871},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5478644967079163},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5327057242393494},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.5324227809906006},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5041669607162476},{"id":"https://openalex.org/keywords/interval","display_name":"Interval (graph theory)","score":0.4992392063140869},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.4827210605144501},{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.44117286801338196},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.42435887455940247},{"id":"https://openalex.org/keywords/long-short-term-memory","display_name":"Long short term memory","score":0.4221605360507965},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3471047580242157}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7479928731918335},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6115260720252991},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5900416374206543},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5839521288871765},{"id":"https://openalex.org/C119898033","wikidata":"https://www.wikidata.org/wiki/Q3433888","display_name":"Ensemble forecasting","level":2,"score":0.5780846476554871},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5478644967079163},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5327057242393494},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.5324227809906006},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5041669607162476},{"id":"https://openalex.org/C2778067643","wikidata":"https://www.wikidata.org/wiki/Q166507","display_name":"Interval (graph theory)","level":2,"score":0.4992392063140869},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.4827210605144501},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.44117286801338196},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.42435887455940247},{"id":"https://openalex.org/C133488467","wikidata":"https://www.wikidata.org/wiki/Q6673524","display_name":"Long short term memory","level":4,"score":0.4221605360507965},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3471047580242157},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icoin50884.2021.9333853","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icoin50884.2021.9333853","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Conference on Information Networking (ICOIN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4699999988079071,"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320320671","display_name":"National Research Foundation","ror":"https://ror.org/05s0g1g46"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W1986407511","https://openalex.org/W2031015560","https://openalex.org/W2040842705","https://openalex.org/W2043007983","https://openalex.org/W2057248704","https://openalex.org/W2064675550","https://openalex.org/W2172064003","https://openalex.org/W2174972094","https://openalex.org/W2547676993","https://openalex.org/W2571811882","https://openalex.org/W2601741161","https://openalex.org/W2611050176","https://openalex.org/W2806110175","https://openalex.org/W2810151852","https://openalex.org/W2952431225","https://openalex.org/W2955981725","https://openalex.org/W2974558844","https://openalex.org/W2999882229","https://openalex.org/W6735859500"],"related_works":["https://openalex.org/W2912153778","https://openalex.org/W2794896638","https://openalex.org/W4288108708","https://openalex.org/W2891633941","https://openalex.org/W4387163678","https://openalex.org/W2973430807","https://openalex.org/W4385280324","https://openalex.org/W2890685186","https://openalex.org/W2984436043","https://openalex.org/W4390245176"],"abstract_inverted_index":{"Time":[0],"series":[1],"prediction":[2],"plays":[3],"a":[4,72],"significant":[5],"role":[6],"in":[7,33,110],"the":[8,77,83],"Bitcoin":[9,54],"market":[10],"because":[11],"of":[12],"volatile":[13],"characteristics.":[14],"Recently,":[15],"deep":[16],"neural":[17],"networks":[18],"with":[19,45,97],"advanced":[20],"techniques":[21],"such":[22,113],"as":[23,114],"ensembles":[24],"have":[25],"led":[26],"to":[27,64],"studies":[28],"that":[29,102],"show":[30,101],"successful":[31],"performance":[32],"various":[34,46],"fields.":[35],"In":[36],"this":[37,103],"paper,":[38],"an":[39],"ensemble-enabled":[40,78],"Long":[41],"Short-Term":[42],"Memory":[43],"(LSTM)":[44],"time":[47,112],"interval":[48],"models":[49],"is":[50],"proposed":[51],"for":[52],"predicting":[53],"price.":[55],"Although":[56],"hour":[57],"and":[58,86],"minute":[59],"data":[60,69,93,100],"set":[61],"are":[62],"shown":[63],"provide":[65],"moderate":[66],"shifts,":[67],"daily":[68],"has":[70],"relatively":[71],"deterministic":[73],"shift.":[74],"As":[75],"such,":[76],"LSTM":[79],"network":[80],"architecture":[81,105],"learned":[82],"individual":[84],"characteristics":[85],"impact":[87],"on":[88],"price":[89,116],"predictions":[90],"from":[91],"each":[92],"set.":[94],"Experimental":[95],"results":[96],"real-world":[98],"measurement":[99],"learning":[104],"effectively":[106],"forecasts":[107],"prices,":[108],"especially":[109],"risky":[111],"sudden":[115],"fall.":[117]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":10},{"year":2023,"cited_by_count":9},{"year":2022,"cited_by_count":8},{"year":2021,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
