{"id":"https://openalex.org/W2810209452","doi":"https://doi.org/10.1109/fskd.2017.8393351","title":"Bitcoin price prediction using ensembles of neural networks","display_name":"Bitcoin price prediction using ensembles of neural networks","publication_year":2017,"publication_date":"2017-07-01","ids":{"openalex":"https://openalex.org/W2810209452","doi":"https://doi.org/10.1109/fskd.2017.8393351","mag":"2810209452"},"language":"en","primary_location":{"id":"doi:10.1109/fskd.2017.8393351","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fskd.2017.8393351","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","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/A5058739806","display_name":"Edwin Sin","orcid":null},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":true,"raw_author_name":"Edwin Sin","raw_affiliation_strings":["School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore"],"affiliations":[{"raw_affiliation_string":"School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore","institution_ids":["https://openalex.org/I172675005"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5086764741","display_name":"Lipo Wang","orcid":"https://orcid.org/0000-0002-4257-7639"},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Lipo Wang","raw_affiliation_strings":["School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore"],"affiliations":[{"raw_affiliation_string":"School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore","institution_ids":["https://openalex.org/I172675005"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5058739806"],"corresponding_institution_ids":["https://openalex.org/I172675005"],"apc_list":null,"apc_paid":null,"fwci":6.8257,"has_fulltext":false,"cited_by_count":133,"citation_normalized_percentile":{"value":0.9745474,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"666","last_page":"671"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T14351","display_name":"Statistical and Computational Modeling","score":0.989799976348877,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T14351","display_name":"Statistical and Computational Modeling","score":0.989799976348877,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T10320","display_name":"Neural Networks and Applications","score":0.9861999750137329,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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":0.9847999811172485,"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":0.6601524353027344},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6596190929412842},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4818750321865082},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3983851969242096}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6601524353027344},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6596190929412842},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4818750321865082},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3983851969242096}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/fskd.2017.8393351","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fskd.2017.8393351","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":70,"referenced_works":["https://openalex.org/W1497414364","https://openalex.org/W1549306454","https://openalex.org/W1584569974","https://openalex.org/W1613269347","https://openalex.org/W1798106456","https://openalex.org/W2007907687","https://openalex.org/W2019207321","https://openalex.org/W2029864452","https://openalex.org/W2036599383","https://openalex.org/W2037935179","https://openalex.org/W2044925139","https://openalex.org/W2054902599","https://openalex.org/W2077937929","https://openalex.org/W2090091537","https://openalex.org/W2097169398","https://openalex.org/W2097554207","https://openalex.org/W2099975513","https://openalex.org/W2100128988","https://openalex.org/W2100253618","https://openalex.org/W2100383651","https://openalex.org/W2105393881","https://openalex.org/W2108423494","https://openalex.org/W2109099170","https://openalex.org/W2114404520","https://openalex.org/W2123513648","https://openalex.org/W2124256364","https://openalex.org/W2137864425","https://openalex.org/W2151419026","https://openalex.org/W2155806188","https://openalex.org/W2158569465","https://openalex.org/W2163611833","https://openalex.org/W2164512879","https://openalex.org/W2183183828","https://openalex.org/W2232827647","https://openalex.org/W2236966765","https://openalex.org/W2334160455","https://openalex.org/W2345703512","https://openalex.org/W2369419998","https://openalex.org/W2401286758","https://openalex.org/W2404812126","https://openalex.org/W2514328648","https://openalex.org/W2516938563","https://openalex.org/W2534650598","https://openalex.org/W2556046789","https://openalex.org/W2562172273","https://openalex.org/W2582163918","https://openalex.org/W2586651977","https://openalex.org/W2603530161","https://openalex.org/W2604772753","https://openalex.org/W2605389255","https://openalex.org/W2728943311","https://openalex.org/W2743729757","https://openalex.org/W2911964244","https://openalex.org/W2962822615","https://openalex.org/W3124555749","https://openalex.org/W3143204426","https://openalex.org/W4248175462","https://openalex.org/W4285719527","https://openalex.org/W4302081184","https://openalex.org/W6629568988","https://openalex.org/W6674406337","https://openalex.org/W6676156182","https://openalex.org/W6676552757","https://openalex.org/W6678153858","https://openalex.org/W6680308420","https://openalex.org/W6703061134","https://openalex.org/W6712922992","https://openalex.org/W6713526773","https://openalex.org/W6735856622","https://openalex.org/W6742322174"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W3046775127","https://openalex.org/W4394896187","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W3107602296","https://openalex.org/W4364306694","https://openalex.org/W4312192474","https://openalex.org/W4283697347"],"abstract_inverted_index":{"This":[0],"paper":[1],"explores":[2],"the":[3,6,11,16,40,46,50,55,63,69,74,86,105,128,147,153],"relationship":[4],"between":[5],"features":[7,84],"of":[8,18,45,73,76,81,85,91,97],"Bitcoin":[9,19,77],"and":[10,57,141],"next":[12,70],"day":[13,71,112,130],"change":[14],"in":[15,49,60,152,159],"price":[17,75],"using":[20,36],"an":[21,137],"Artificial":[22],"Neural":[23,32],"Network":[24,33],"ensemble":[25,64,106,154],"approach":[26],"called":[27],"Genetic":[28],"Algorithm":[29],"based":[30,103],"Selective":[31],"Ensemble,":[34],"constructed":[35],"Multi-Layered":[37],"Perceptron":[38],"as":[39],"base":[41],"model":[42,151],"for":[43],"each":[44],"neural":[47],"network":[48],"ensemble.":[51],"To":[52],"better":[53],"understand":[54],"practicality":[56],"its":[58],"effectiveness":[59],"real-world":[61],"application,":[62],"was":[65,107],"used":[66],"to":[67],"predict":[68],"direction":[72],"given":[78],"a":[79,89,95,100,110,142],"set":[80],"approximately":[82,157],"200":[83],"cryptocurrency":[87],"over":[88],"span":[90,96],"2":[92],"years.":[93],"Over":[94],"50":[98],"days,":[99],"trading":[101,115,121,133,143],"strategy":[102,116,122,134,144],"on":[104],"compared":[108],"against":[109],"\u201cprevious":[111,129],"trend":[113,131],"following\u201d":[114,132],"through":[117],"back-testing.":[118],"The":[119],"former":[120],"generated":[123,156],"almost":[124],"85%":[125],"returns,":[126],"outperforming":[127],"which":[135],"produced":[136],"approximate":[138],"38%":[139],"returns":[140],"that":[145,155],"follows":[146],"single,":[148],"best":[149],"MLP":[150],"53%":[158],"returns.":[160]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":9},{"year":2024,"cited_by_count":18},{"year":2023,"cited_by_count":24},{"year":2022,"cited_by_count":24},{"year":2021,"cited_by_count":22},{"year":2020,"cited_by_count":20},{"year":2019,"cited_by_count":15}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
