{"id":"https://openalex.org/W4307996341","doi":"https://doi.org/10.3390/info13110524","title":"CryptoNet: Using Auto-Regressive Multi-Layer Artificial Neural Networks to Predict Financial Time Series","display_name":"CryptoNet: Using Auto-Regressive Multi-Layer Artificial Neural Networks to Predict Financial Time Series","publication_year":2022,"publication_date":"2022-11-02","ids":{"openalex":"https://openalex.org/W4307996341","doi":"https://doi.org/10.3390/info13110524"},"language":"en","primary_location":{"id":"doi:10.3390/info13110524","doi":"https://doi.org/10.3390/info13110524","is_oa":true,"landing_page_url":"https://doi.org/10.3390/info13110524","pdf_url":"https://www.mdpi.com/2078-2489/13/11/524/pdf?version=1667397348","source":{"id":"https://openalex.org/S4210219776","display_name":"Information","issn_l":"2078-2489","issn":["2078-2489"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["anvur-class-a","anvur-scientific","cwts-core","doaj","norway-1","poland-40","russia-white-list-2","vabb-shw"],"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Information","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2078-2489/13/11/524/pdf?version=1667397348","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5062023845","display_name":"Leonardo Ranaldi","orcid":"https://orcid.org/0000-0001-8488-4146","observed_orcids":["https://orcid.org/0000-0001-8488-4146"]},"institutions":[{"id":"https://openalex.org/I116067653","display_name":"University of Rome Tor Vergata","ror":"https://ror.org/02p77k626","country_code":"IT","type":"education","lineage":["https://openalex.org/I116067653"]},{"id":"https://openalex.org/I2801039369","display_name":"Marconi University","ror":"https://ror.org/00j0rk173","country_code":"IT","type":"education","lineage":["https://openalex.org/I2801039369"]}],"countries":["IT"],"is_corresponding":true,"raw_author_name":"Leonardo Ranaldi","raw_affiliation_strings":["Department of Enterprise Engineering, University of Rome Tor Vergata, 00133 Rome, Italy","Department of Innovation and Information Engineering, Guglielmo Marconi University, 00193 Roma, Italy"],"raw_orcid":"https://orcid.org/0000-0001-8488-4146","affiliations":[{"raw_affiliation_string":"Department of Enterprise Engineering, University of Rome Tor Vergata, 00133 Rome, Italy","institution_ids":["https://openalex.org/I116067653"]},{"raw_affiliation_string":"Department of Innovation and Information Engineering, Guglielmo Marconi University, 00193 Roma, Italy","institution_ids":["https://openalex.org/I2801039369"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078138160","display_name":"Marco Gerardi","orcid":"https://orcid.org/0000-0002-1784-7334","observed_orcids":["https://orcid.org/0000-0002-1784-7334"]},"institutions":[{"id":"https://openalex.org/I2801039369","display_name":"Marconi University","ror":"https://ror.org/00j0rk173","country_code":"IT","type":"education","lineage":["https://openalex.org/I2801039369"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Marco Gerardi","raw_affiliation_strings":["Department of Innovation and Information Engineering, Guglielmo Marconi University, 00193 Roma, Italy"],"raw_orcid":"https://orcid.org/0000-0002-1784-7334","affiliations":[{"raw_affiliation_string":"Department of Innovation and Information Engineering, Guglielmo Marconi University, 00193 Roma, Italy","institution_ids":["https://openalex.org/I2801039369"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5024200617","display_name":"Francesca Fallucchi","orcid":"https://orcid.org/0000-0002-3288-044X","observed_orcids":["https://orcid.org/0000-0002-3288-044X"]},"institutions":[{"id":"https://openalex.org/I2801039369","display_name":"Marconi University","ror":"https://ror.org/00j0rk173","country_code":"IT","type":"education","lineage":["https://openalex.org/I2801039369"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Francesca Fallucchi","raw_affiliation_strings":["Department of Innovation and Information Engineering, Guglielmo Marconi University, 00193 Roma, Italy"],"raw_orcid":"https://orcid.org/0000-0002-3288-044X","affiliations":[{"raw_affiliation_string":"Department of Innovation and Information Engineering, Guglielmo Marconi University, 00193 Roma, Italy","institution_ids":["https://openalex.org/I2801039369"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5062023845"],"corresponding_institution_ids":["https://openalex.org/I116067653","https://openalex.org/I2801039369"],"apc_list":{"value":1400,"currency":"CHF","value_usd":1466},"apc_paid":{"value":1400,"currency":"CHF","value_usd":1466},"fwci":3.7029,"has_fulltext":true,"cited_by_count":29,"citation_normalized_percentile":{"value":0.93753484,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":99},"biblio":{"volume":"13","issue":"11","first_page":"524","last_page":"524"},"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/T10270","display_name":"Blockchain Technology Applications and Security","score":0.0,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/financial-time-series-forecasting","display_name":"financial time series forecasting","score":0.7639999985694885},{"id":"https://openalex.org/keywords/cryptocurrency-price-prediction","display_name":"cryptocurrency price prediction","score":0.8320000171661377},{"id":"https://openalex.org/keywords/bitcoin","display_name":"Bitcoin","score":0.8679999709129333},{"id":"https://openalex.org/keywords/ethereum","display_name":"Ethereum","score":0.921999990940094},{"id":"https://openalex.org/keywords/multilayer-neural-networks","display_name":"multilayer neural networks","score":0.7919999957084656},{"id":"https://openalex.org/keywords/risk-reward-ratio","display_name":"risk-reward ratio","score":0.8140000104904175},{"id":"https://openalex.org/keywords/trading-strategies","display_name":"trading strategies","score":0.4009999930858612}],"concepts":[{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6921345591545105},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6624338030815125},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.648650050163269},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5973175168037415},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5963146686553955},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.545680820941925},{"id":"https://openalex.org/C175706884","wikidata":"https://www.wikidata.org/wiki/Q1130194","display_name":"Moving average","level":2,"score":0.5200200080871582},{"id":"https://openalex.org/C74883015","wikidata":"https://www.wikidata.org/wiki/Q290467","display_name":"Autoregressive\u2013moving-average model","level":3,"score":0.5060133337974548},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.4814974069595337},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.3774987757205963},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.35214871168136597},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.16688290238380432},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.12179073691368103},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.3390/info13110524","doi":"https://doi.org/10.3390/info13110524","is_oa":true,"landing_page_url":"https://doi.org/10.3390/info13110524","pdf_url":"https://www.mdpi.com/2078-2489/13/11/524/pdf?version=1667397348","source":{"id":"https://openalex.org/S4210219776","display_name":"Information","issn_l":"2078-2489","issn":["2078-2489"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["anvur-class-a","anvur-scientific","cwts-core","doaj","norway-1","poland-40","russia-white-list-2","vabb-shw"],"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Information","raw_type":"journal-article"},{"id":"pmh:oai:art.torvergata.it:2108/423985","doi":null,"is_oa":false,"landing_page_url":"https://hdl.handle.net/2108/423985","pdf_url":null,"source":{"id":"https://openalex.org/S4306400993","display_name":"Cineca Institutional Research Information System (Tor Vergata University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I116067653","host_organization_name":"University of Rome Tor Vergata","host_organization_lineage":["https://openalex.org/I116067653"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"info:eu-repo/semantics/article"},{"id":"pmh:oai:doaj.org/article:6b85f19620b84dd1a99a50d3c7af3a0f","doi":null,"is_oa":true,"landing_page_url":"https://doaj.org/article/6b85f19620b84dd1a99a50d3c7af3a0f","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Information, Vol 13, Iss 11, p 524 (2022)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3390/info13110524","doi":"https://doi.org/10.3390/info13110524","is_oa":true,"landing_page_url":"https://doi.org/10.3390/info13110524","pdf_url":"https://www.mdpi.com/2078-2489/13/11/524/pdf?version=1667397348","source":{"id":"https://openalex.org/S4210219776","display_name":"Information","issn_l":"2078-2489","issn":["2078-2489"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["anvur-class-a","anvur-scientific","cwts-core","doaj","norway-1","poland-40","russia-white-list-2","vabb-shw"],"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Information","raw_type":"journal-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4307996341.pdf","grobid_xml":"https://content.openalex.org/works/W4307996341.grobid-xml"},"referenced_works_count":20,"referenced_works":["https://openalex.org/W2025053102","https://openalex.org/W2032170121","https://openalex.org/W2068805783","https://openalex.org/W2182617889","https://openalex.org/W2469386107","https://openalex.org/W2484997644","https://openalex.org/W2616248709","https://openalex.org/W2621918501","https://openalex.org/W2938308298","https://openalex.org/W3033882778","https://openalex.org/W3094361099","https://openalex.org/W3109528744","https://openalex.org/W3116184845","https://openalex.org/W3122070388","https://openalex.org/W3124918396","https://openalex.org/W3177056500","https://openalex.org/W4221022855","https://openalex.org/W4226020815","https://openalex.org/W4226059755","https://openalex.org/W4291291810"],"related_works":["https://openalex.org/W4226210516","https://openalex.org/W4206831055","https://openalex.org/W3046092664","https://openalex.org/W6992659070","https://openalex.org/W3167149419","https://openalex.org/W4386224525","https://openalex.org/W2966373834","https://openalex.org/W6988294156","https://openalex.org/W4411357644","https://openalex.org/W6960016827"],"abstract_inverted_index":{"When":[0],"analyzing":[1],"a":[2,90],"financial":[3,55],"asset,":[4],"it":[5],"is":[6,17,58,116],"essential":[7],"to":[8,20,28,53,59,168,188],"study":[9,54],"the":[10,49,95,119,124,162],"trend":[11],"of":[12,48,127,170],"its":[13,22,31,61,104],"time":[14,27,56,120,139],"series.":[15],"It":[16],"also":[18],"necessary":[19],"examine":[21],"evolution":[23],"and":[24,37,72,93,106,131,150],"activity":[25],"over":[26],"statistically":[29],"analyze":[30,60],"possible":[32],"future":[33],"behavior.":[34],"Both":[35],"retail":[36],"institutional":[38],"investors":[39],"base":[40],"their":[41],"trading":[42],"strategies":[43],"on":[44,118,138,152],"these":[45],"analyses.":[46],"One":[47],"most":[50],"used":[51],"techniques":[52,183],"series":[57,121,140],"dynamic":[62],"structure":[63],"using":[64],"auto-regressive":[65,74],"models,":[66],"simple":[67],"moving":[68,75],"average":[69,76],"models":[70,77],"(SMA),":[71],"mixed":[73],"(ARMA).":[78],"These":[79],"techniques,":[80],"unfortunately,":[81],"do":[82],"not":[83],"always":[84],"provide":[85],"appreciable":[86],"results":[87],"both":[88],"at":[89],"statistical":[91],"level":[92],"as":[94],"Risk-Reward":[96],"Ratio":[97],"(RRR);":[98],"above":[99],"all,":[100],"each":[101],"system":[102,115],"has":[103],"pros":[105],"cons.":[107],"In":[108],"this":[109,114,177],"paper,":[110],"we":[111,136],"present":[112],"CryptoNet;":[113],"based":[117],"extraction":[122,142],"exploiting":[123],"vast":[125],"potential":[126],"artificial":[128,146],"intelligence":[129],"(AI)":[130],"machine":[132,181],"learning":[133,159,182],"(ML).":[134],"Specifically,":[135],"focused":[137],"trends":[141],"by":[143],"developing":[144],"an":[145],"neural":[147],"network,":[148],"trained":[149],"tested":[151],"two":[153],"famous":[154],"crypto-currencies:":[155],"Bitcoinand":[156],"Ether.":[157],"CryptoNet":[158],"algorithm":[160],"improved":[161],"classic":[163],"linear":[164],"regression":[165],"model":[166],"up":[167],"31%":[169],"MAE":[171],"(mean":[172],"absolute":[173],"error).":[174],"Results":[175],"from":[176],"work":[178],"should":[179],"encourage":[180],"in":[184],"sectors":[185],"classically":[186],"reluctant":[187],"adopt":[189],"non-standard":[190],"approaches.":[191]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":12},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":11}],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2022-11-07T00:00:00"}
