{"id":"https://openalex.org/W7164862945","doi":"https://doi.org/10.1145/3816713.3818216","title":"Machine Learning Models for Multi-Horizon Classification of Bitcoin Future Price Movements","display_name":"Machine Learning Models for Multi-Horizon Classification of Bitcoin Future Price Movements","publication_year":2026,"publication_date":"2026-06-15","ids":{"openalex":"https://openalex.org/W7164862945","doi":"https://doi.org/10.1145/3816713.3818216"},"language":null,"primary_location":{"id":"doi:10.1145/3816713.3818216","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3816713.3818216","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 14th International Conference on Advances in Information Technology","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3816713.3818216","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5042231884","display_name":"Sirawat Boonpai","orcid":null},"institutions":[{"id":"https://openalex.org/I91538806","display_name":"King Mongkut's Institute of Technology Ladkrabang","ror":"https://ror.org/055mf0v62","country_code":"TH","type":"education","lineage":["https://openalex.org/I91538806"]}],"countries":["TH"],"is_corresponding":false,"raw_author_name":"Sirawat Boonpai","raw_affiliation_strings":["School of Information Technology, King Mongkut's Institute of Technology Ladkrabang, Lat Krabang, Bangkok, Thailand"],"raw_orcid":"https://orcid.org/0009-0005-2986-7331","affiliations":[{"raw_affiliation_string":"School of Information Technology, King Mongkut's Institute of Technology Ladkrabang, Lat Krabang, Bangkok, Thailand","institution_ids":["https://openalex.org/I91538806"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083148508","display_name":"Ponrudee Netisopakul","orcid":"https://orcid.org/0000-0003-1409-9729"},"institutions":[{"id":"https://openalex.org/I91538806","display_name":"King Mongkut's Institute of Technology Ladkrabang","ror":"https://ror.org/055mf0v62","country_code":"TH","type":"education","lineage":["https://openalex.org/I91538806"]}],"countries":["TH"],"is_corresponding":false,"raw_author_name":"Ponrudee Netisopakul","raw_affiliation_strings":["School of Information Technology, King Mongkut's Institute of Technology Ladkrabang, Lat Krabang, Bangkok, Thailand"],"raw_orcid":"https://orcid.org/0000-0003-1409-9729","affiliations":[{"raw_affiliation_string":"School of Information Technology, King Mongkut's Institute of Technology Ladkrabang, Lat Krabang, Bangkok, Thailand","institution_ids":["https://openalex.org/I91538806"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5037562656","display_name":"Anuntapat Anuntachai","orcid":null},"institutions":[{"id":"https://openalex.org/I91538806","display_name":"King Mongkut's Institute of Technology Ladkrabang","ror":"https://ror.org/055mf0v62","country_code":"TH","type":"education","lineage":["https://openalex.org/I91538806"]}],"countries":["TH"],"is_corresponding":false,"raw_author_name":"Anuntapat Anuntachai","raw_affiliation_strings":["School of Information Technology, King Mongkut's Institute of Technology Ladkrabang, Lat Krabang, Bangkok, Thailand"],"raw_orcid":"https://orcid.org/0009-0007-4555-4727","affiliations":[{"raw_affiliation_string":"School of Information Technology, King Mongkut's Institute of Technology Ladkrabang, Lat Krabang, Bangkok, Thailand","institution_ids":["https://openalex.org/I91538806"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I91538806"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.91598265,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10270","display_name":"Blockchain Technology Applications and Security","score":0.7484999895095825,"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":0.7484999895095825,"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/T11326","display_name":"Stock Market Forecasting Methods","score":0.13339999318122864,"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/T11059","display_name":"Market Dynamics and Volatility","score":0.01759999990463257,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.6502000093460083},{"id":"https://openalex.org/keywords/binary-classification","display_name":"Binary classification","score":0.637499988079071},{"id":"https://openalex.org/keywords/futures-contract","display_name":"Futures contract","score":0.559499979019165},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5296000242233276},{"id":"https://openalex.org/keywords/binary-number","display_name":"Binary number","score":0.460099995136261},{"id":"https://openalex.org/keywords/volatility","display_name":"Volatility (finance)","score":0.44029998779296875},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.4251999855041504},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.4242999851703644}],"concepts":[{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.7289999723434448},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7063999772071838},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6620000004768372},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.6502000093460083},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.637499988079071},{"id":"https://openalex.org/C106306483","wikidata":"https://www.wikidata.org/wiki/Q183984","display_name":"Futures contract","level":2,"score":0.559499979019165},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5296000242233276},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.460099995136261},{"id":"https://openalex.org/C91602232","wikidata":"https://www.wikidata.org/wiki/Q756115","display_name":"Volatility (finance)","level":2,"score":0.44029998779296875},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.4251999855041504},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.4242999851703644},{"id":"https://openalex.org/C13460635","wikidata":"https://www.wikidata.org/wiki/Q85753676","display_name":"Classification scheme","level":2,"score":0.41769999265670776},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4108000099658966},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.40209999680519104},{"id":"https://openalex.org/C110083411","wikidata":"https://www.wikidata.org/wiki/Q1744628","display_name":"Statistical classification","level":2,"score":0.3889000117778778},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.366100013256073},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.3294999897480011},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.30169999599456787},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.29899999499320984},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.27970001101493835},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.266400009393692},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.26350000500679016},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.257099986076355},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.25380000472068787}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3816713.3818216","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3816713.3818216","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 14th International Conference on Advances in Information Technology","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3816713.3818216","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3816713.3818216","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 14th International Conference on Advances in Information Technology","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.4743173122406006,"display_name":"Life in Land","id":"https://metadata.un.org/sdg/15"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W2156665896","https://openalex.org/W2170505850","https://openalex.org/W3015481177","https://openalex.org/W3038584219","https://openalex.org/W3124647634","https://openalex.org/W4315853251","https://openalex.org/W4323846851","https://openalex.org/W4376115431","https://openalex.org/W4385060446","https://openalex.org/W4401349711","https://openalex.org/W4414613871"],"related_works":[],"abstract_inverted_index":{"This":[0],"study":[1,66,177],"presents":[2],"a":[3,61,68,162,212],"comprehensive":[4],"framework":[5],"for":[6,88,130,204,215],"multi-horizon":[7],"classification":[8,39,83,96,181],"of":[9,19,48,79,112,121,155],"Bitcoin":[10,89,122,208],"futures":[11,209],"price":[12,119,156],"movements":[13,168],"using":[14],"machine":[15],"learning.":[16],"Five":[17],"models":[18],"Logistic":[20],"Regression,":[21],"Random":[22,134],"Forest,":[23],"XGBoost,":[24],"LightGBM,":[25],"and":[26,37,43,51,82,188,210],"CatBoost":[27,125],"were":[28],"systematically":[29],"evaluated":[30],"across":[31,72,141],"three":[32],"timeframes":[33,104],"(4h,":[34],"12h,":[35],"1d)":[36],"four":[38],"schemes:":[40],"binary":[41,95,131],"(up/down)":[42],"multi-class":[44,173],"(up/down/stable)":[45],"with":[46,102],"thresholds":[47,182,193],"0.5%,":[49],"1.0%,":[50],"1.5%,":[52],"totaling":[53],"60":[54],"distinct":[55],"experimental":[56],"configurations.":[57,143],"Rather":[58],"than":[59],"pursuing":[60],"strong":[62],"predictive":[63,100],"performance,":[64,101,190],"this":[65],"prioritizes":[67],"rigorous":[69],"comparative":[70],"analysis":[71,146],"multiple":[73],"dimensions":[74],"to":[75],"identify":[76],"which":[77],"combinations":[78],"model,":[80],"timeframe,":[81],"scheme":[84],"are":[85,151],"most":[86,138],"effective":[87],"futures.":[90,123],"The":[91],"results":[92],"demonstrate":[93],"that":[94,148,179],"achieves":[97],"the":[98,110,117,127,137,152,176],"best":[99],"shorter":[103],"yielding":[105],"significantly":[106],"better":[107],"results,":[108],"confirming":[109],"effectiveness":[111],"technical":[113],"indicators":[114,150],"in":[115,165,172,207],"capturing":[116],"rapid":[118],"dynamics":[120],"Notably,":[124],"achieved":[126],"highest":[128],"F1-score":[129],"classification,":[132],"while":[133,158],"Forest":[135],"proved":[136],"robust":[139],"model":[140],"diverse":[142],"Feature":[144],"importance":[145],"revealed":[147],"momentum-based":[149],"dominant":[153],"predictors":[154],"direction,":[157],"volatility":[159],"features":[160],"play":[161],"critical":[163],"role":[164],"distinguishing":[166],"sideways":[167],"from":[169],"directional":[170],"ones":[171],"settings.":[174],"Furthermore,":[175],"demonstrates":[178],"narrower":[180],"(0.5%)":[183],"introduce":[184],"noisier":[185],"class":[186],"boundaries":[187],"degrade":[189],"whereas":[191],"wider":[192],"(1.0%\u20131.5%)":[194],"yield":[195],"more":[196],"stable":[197],"results.":[198],"These":[199],"findings":[200],"provide":[201],"actionable":[202],"guidelines":[203],"algorithmic":[205],"trading":[206],"establish":[211],"reproducible":[213],"benchmark":[214],"future":[216],"research.":[217]},"counts_by_year":[],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2026-06-16T00:00:00"}