{"id":"https://openalex.org/W7118628256","doi":"https://doi.org/10.7717/peerj-cs.3472","title":"Multi-step-ahead forecasting of bike-sharing demand using multilayer perceptron model with additional timestamp features","display_name":"Multi-step-ahead forecasting of bike-sharing demand using multilayer perceptron model with additional timestamp features","publication_year":2026,"publication_date":"2026-01-07","ids":{"openalex":"https://openalex.org/W7118628256","doi":"https://doi.org/10.7717/peerj-cs.3472"},"language":"en","primary_location":{"id":"doi:10.7717/peerj-cs.3472","is_oa":true,"landing_page_url":"https://doi.org/10.7717/peerj-cs.3472","pdf_url":null,"source":{"id":"https://openalex.org/S4210178049","display_name":"PeerJ Computer Science","issn_l":"2376-5992","issn":["2376-5992"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320104","host_organization_name":"PeerJ, Inc.","host_organization_lineage":["https://openalex.org/P4310320104"],"host_organization_lineage_names":["PeerJ, Inc."],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"PeerJ Computer Science","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.7717/peerj-cs.3472","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5028810282","display_name":"Ganjar Alfian","orcid":"https://orcid.org/0000-0002-3273-1452"},"institutions":[{"id":"https://openalex.org/I165230279","display_name":"Universitas Gadjah Mada","ror":"https://ror.org/03ke6d638","country_code":"ID","type":"education","lineage":["https://openalex.org/I165230279"]}],"countries":["ID"],"is_corresponding":true,"raw_author_name":"Ganjar Alfian","raw_affiliation_strings":["Department of Electrical Engineering and Informatics, Vocational College, Universitas Gadjah Mada, Yogyakarta, Indonesia"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering and Informatics, Vocational College, Universitas Gadjah Mada, Yogyakarta, Indonesia","institution_ids":["https://openalex.org/I165230279"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031383490","display_name":"Yuris Mulya Saputra","orcid":"https://orcid.org/0000-0001-8862-2004"},"institutions":[{"id":"https://openalex.org/I165230279","display_name":"Universitas Gadjah Mada","ror":"https://ror.org/03ke6d638","country_code":"ID","type":"education","lineage":["https://openalex.org/I165230279"]}],"countries":["ID"],"is_corresponding":false,"raw_author_name":"Yuris Mulya Saputra","raw_affiliation_strings":["Department of Electrical Engineering and Informatics, Vocational College, Universitas Gadjah Mada, Yogyakarta, Indonesia"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering and Informatics, Vocational College, Universitas Gadjah Mada, Yogyakarta, Indonesia","institution_ids":["https://openalex.org/I165230279"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5122077907","display_name":"Wildan Dzaky Ramadhani","orcid":null},"institutions":[{"id":"https://openalex.org/I165230279","display_name":"Universitas Gadjah Mada","ror":"https://ror.org/03ke6d638","country_code":"ID","type":"education","lineage":["https://openalex.org/I165230279"]}],"countries":["ID"],"is_corresponding":false,"raw_author_name":"Wildan Dzaky Ramadhani","raw_affiliation_strings":["Department of Electrical Engineering and Informatics, Vocational College, Universitas Gadjah Mada, Yogyakarta, Indonesia"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering and Informatics, Vocational College, Universitas Gadjah Mada, Yogyakarta, Indonesia","institution_ids":["https://openalex.org/I165230279"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030041099","display_name":"Fransiskus Tatas Dwi Atmaji","orcid":"https://orcid.org/0000-0002-1437-1721"},"institutions":[{"id":"https://openalex.org/I862893732","display_name":"Telkom University","ror":"https://ror.org/0004wsx81","country_code":"ID","type":"education","lineage":["https://openalex.org/I862893732"]}],"countries":["ID"],"is_corresponding":false,"raw_author_name":"Fransiskus Tatas Dwi Atmaji","raw_affiliation_strings":["Industrial Engineering Study Program, School of Industrial Engineering, Telkom University, Main Campus (Bandung Campus),  Bandung, Indonesia"],"affiliations":[{"raw_affiliation_string":"Industrial Engineering Study Program, School of Industrial Engineering, Telkom University, Main Campus (Bandung Campus),  Bandung, Indonesia","institution_ids":["https://openalex.org/I862893732"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038063757","display_name":"Umar Farooq","orcid":"https://orcid.org/0000-0003-0828-2842"},"institutions":[{"id":"https://openalex.org/I73417466","display_name":"Coventry University","ror":"https://ror.org/01tgmhj36","country_code":"GB","type":"education","lineage":["https://openalex.org/I73417466"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Umar Farooq","raw_affiliation_strings":["Faculty of Business and Law, Coventry University, Coventry, United Kingdom"],"affiliations":[{"raw_affiliation_string":"Faculty of Business and Law, Coventry University, Coventry, United Kingdom","institution_ids":["https://openalex.org/I73417466"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019587662","display_name":"Filip Bene\u0161","orcid":"https://orcid.org/0000-0001-9997-6078"},"institutions":[{"id":"https://openalex.org/I142208455","display_name":"VSB - Technical University of Ostrava","ror":"https://ror.org/05x8mcb75","country_code":"CZ","type":"education","lineage":["https://openalex.org/I142208455"]}],"countries":["CZ"],"is_corresponding":false,"raw_author_name":"Filip Benes","raw_affiliation_strings":["Department of Economics and Control Systems, Faculty of Mining and Geology, VSB\u2014Technical University of Ostrava, Ostrava, Czech Republic"],"affiliations":[{"raw_affiliation_string":"Department of Economics and Control Systems, Faculty of Mining and Geology, VSB\u2014Technical University of Ostrava, Ostrava, Czech Republic","institution_ids":["https://openalex.org/I142208455"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5094333420","display_name":"Norma Latif FItriyani","orcid":null},"institutions":[{"id":"https://openalex.org/I28777354","display_name":"Sejong University","ror":"https://ror.org/00aft1q37","country_code":"KR","type":"education","lineage":["https://openalex.org/I28777354"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Norma Latif Fitriyani","raw_affiliation_strings":["Department of Artificial Intelligence and Data Science, Sejong University, Seoul, South Korea"],"affiliations":[{"raw_affiliation_string":"Department of Artificial Intelligence and Data Science, Sejong University, Seoul, South Korea","institution_ids":["https://openalex.org/I28777354"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5122171669","display_name":"Muhammad Syafrudin","orcid":null},"institutions":[{"id":"https://openalex.org/I28777354","display_name":"Sejong University","ror":"https://ror.org/00aft1q37","country_code":"KR","type":"education","lineage":["https://openalex.org/I28777354"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Muhammad Syafrudin","raw_affiliation_strings":["Department of Artificial Intelligence and Data Science, Sejong University, Seoul, South Korea"],"affiliations":[{"raw_affiliation_string":"Department of Artificial Intelligence and Data Science, Sejong University, Seoul, South Korea","institution_ids":["https://openalex.org/I28777354"]}]}],"institutions":[],"countries_distinct_count":4,"institutions_distinct_count":8,"corresponding_author_ids":["https://openalex.org/A5028810282"],"corresponding_institution_ids":["https://openalex.org/I165230279"],"apc_list":{"value":1395,"currency":"USD","value_usd":1395},"apc_paid":{"value":1395,"currency":"USD","value_usd":1395},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.06693307,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"12","issue":null,"first_page":"e3472","last_page":"e3472"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10298","display_name":"Urban Transport and Accessibility","score":0.9703999757766724,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T10298","display_name":"Urban Transport and Accessibility","score":0.9703999757766724,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11824","display_name":"Injury Epidemiology and Prevention","score":0.0032999999821186066,"subfield":{"id":"https://openalex.org/subfields/2739","display_name":"Public Health, Environmental and Occupational Health"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.0020000000949949026,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/timestamp","display_name":"Timestamp","score":0.7042999863624573},{"id":"https://openalex.org/keywords/multilayer-perceptron","display_name":"Multilayer perceptron","score":0.5777000188827515},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.5651999711990356},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.5335000157356262},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.5307000279426575},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.43160000443458557},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.4004000127315521},{"id":"https://openalex.org/keywords/demand-forecasting","display_name":"Demand forecasting","score":0.39899998903274536}],"concepts":[{"id":"https://openalex.org/C113954288","wikidata":"https://www.wikidata.org/wiki/Q186885","display_name":"Timestamp","level":2,"score":0.7042999863624573},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6880999803543091},{"id":"https://openalex.org/C179717631","wikidata":"https://www.wikidata.org/wiki/Q2991667","display_name":"Multilayer perceptron","level":3,"score":0.5777000188827515},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5651999711990356},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.5335000157356262},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.5307000279426575},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5062999725341797},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48010000586509705},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.47440001368522644},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.43160000443458557},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.4004000127315521},{"id":"https://openalex.org/C193809577","wikidata":"https://www.wikidata.org/wiki/Q3409300","display_name":"Demand forecasting","level":2,"score":0.39899998903274536},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.385699987411499},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.3750999867916107},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.37279999256134033},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.3614000082015991},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.34880000352859497},{"id":"https://openalex.org/C2780586970","wikidata":"https://www.wikidata.org/wiki/Q1357284","display_name":"Popularity","level":2,"score":0.3467000126838684},{"id":"https://openalex.org/C167085575","wikidata":"https://www.wikidata.org/wiki/Q6803654","display_name":"Mean squared prediction error","level":2,"score":0.31520000100135803},{"id":"https://openalex.org/C48677424","wikidata":"https://www.wikidata.org/wiki/Q6888088","display_name":"Mode (computer interface)","level":2,"score":0.2955000102519989},{"id":"https://openalex.org/C22679943","wikidata":"https://www.wikidata.org/wiki/Q159375","display_name":"Standard deviation","level":2,"score":0.28859999775886536},{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.2678999900817871},{"id":"https://openalex.org/C107327155","wikidata":"https://www.wikidata.org/wiki/Q330268","display_name":"Decision support system","level":2,"score":0.25859999656677246}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.7717/peerj-cs.3472","is_oa":true,"landing_page_url":"https://doi.org/10.7717/peerj-cs.3472","pdf_url":null,"source":{"id":"https://openalex.org/S4210178049","display_name":"PeerJ Computer Science","issn_l":"2376-5992","issn":["2376-5992"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320104","host_organization_name":"PeerJ, Inc.","host_organization_lineage":["https://openalex.org/P4310320104"],"host_organization_lineage_names":["PeerJ, Inc."],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"PeerJ Computer Science","raw_type":"journal-article"},{"id":"pmh:oai:pure.atira.dk:openaire/ba845b00-350e-4b76-b298-b94cb387f8a1","is_oa":true,"landing_page_url":"https://pureportal.coventry.ac.uk/en/publications/ba845b00-350e-4b76-b298-b94cb387f8a1","pdf_url":null,"source":{"id":"https://openalex.org/S4306402411","display_name":"Pure (Coventry University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I73417466","host_organization_name":"Coventry University","host_organization_lineage":["https://openalex.org/I73417466"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Alfian, G, Saputra, Y M, Ramadhani, W D, Atmaji, F T, Farooq, U, Benes, F, Fitriyani, N L & Syafrudin, M 2026, 'Multi-step-ahead forecasting of bike-sharing demand using multilayer perceptron model with additional timestamp features', PeerJ Computer Science, vol. 12, e3472. https://doi.org/10.7717/peerj-cs.3472","raw_type":"info:eu-repo/semantics/publishedVersion"},{"id":"pmh:oai:doaj.org/article:9d52142695fe4a649086f0269e10751b","is_oa":true,"landing_page_url":"https://doaj.org/article/9d52142695fe4a649086f0269e10751b","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":"PeerJ Computer Science, Vol 12, p e3472 (2026)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.7717/peerj-cs.3472","is_oa":true,"landing_page_url":"https://doi.org/10.7717/peerj-cs.3472","pdf_url":null,"source":{"id":"https://openalex.org/S4210178049","display_name":"PeerJ Computer Science","issn_l":"2376-5992","issn":["2376-5992"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320104","host_organization_name":"PeerJ, Inc.","host_organization_lineage":["https://openalex.org/P4310320104"],"host_organization_lineage_names":["PeerJ, Inc."],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"PeerJ Computer Science","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.7302651405334473}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":61,"referenced_works":["https://openalex.org/W1498436455","https://openalex.org/W1547333707","https://openalex.org/W1841820628","https://openalex.org/W1979411729","https://openalex.org/W2014928429","https://openalex.org/W2060463459","https://openalex.org/W2101234009","https://openalex.org/W2512302015","https://openalex.org/W2608988527","https://openalex.org/W2766977575","https://openalex.org/W2797380693","https://openalex.org/W2883073525","https://openalex.org/W2900019947","https://openalex.org/W2903799353","https://openalex.org/W2961270932","https://openalex.org/W2981046731","https://openalex.org/W3000725205","https://openalex.org/W3005021926","https://openalex.org/W3006129875","https://openalex.org/W3007692608","https://openalex.org/W3011324060","https://openalex.org/W3013238231","https://openalex.org/W3016014946","https://openalex.org/W3022409317","https://openalex.org/W3042918615","https://openalex.org/W3046213253","https://openalex.org/W3106106372","https://openalex.org/W3122720174","https://openalex.org/W3133997628","https://openalex.org/W3183997152","https://openalex.org/W3190844324","https://openalex.org/W3202677566","https://openalex.org/W3213422146","https://openalex.org/W3214588023","https://openalex.org/W4205327523","https://openalex.org/W4206944301","https://openalex.org/W4207043127","https://openalex.org/W4210754377","https://openalex.org/W4230737036","https://openalex.org/W4283714674","https://openalex.org/W4285392842","https://openalex.org/W4289636001","https://openalex.org/W4297248818","https://openalex.org/W4306824892","https://openalex.org/W4313199525","https://openalex.org/W4322102105","https://openalex.org/W4323569278","https://openalex.org/W4372342856","https://openalex.org/W4377092199","https://openalex.org/W4381929541","https://openalex.org/W4383336587","https://openalex.org/W4384472141","https://openalex.org/W4385408355","https://openalex.org/W4385686023","https://openalex.org/W4386183482","https://openalex.org/W4386537395","https://openalex.org/W4386801916","https://openalex.org/W4387842112","https://openalex.org/W4388366321","https://openalex.org/W4399894251","https://openalex.org/W4401560594"],"related_works":[],"abstract_inverted_index":{"Bike":[0],"sharing":[1],"is":[2,243],"increasingly":[3],"gaining":[4],"popularity":[5],"as":[6],"an":[7,38],"affordable":[8],"and":[9,29,115,165,218,228],"environmentally":[10],"friendly":[11],"mode":[12],"of":[13,21,41,51,105,129,203,213,223,251],"transportation":[14],"in":[15,37,186,247],"urban":[16],"areas.":[17],"However,":[18],"the":[19,48,64,84,97,102,109,127,130,134,170,175,198,240,249],"nature":[20],"bike":[22,65],"sharing,":[23],"where":[24],"users":[25],"can":[26],"pick":[27],"up":[28],"return":[30],"bikes":[31,42,53,95,107,253],"at":[32,54,67],"different":[33],"stations,":[34],"often":[35],"results":[36],"uneven":[39],"distribution":[40],"across":[43],"stations.":[44],"Consequently,":[45],"accurately":[46],"predicting":[47],"future":[49,234],"number":[50,250],"rented":[52,88,94,106,238],"each":[55,68],"station":[56],"becomes":[57],"crucial":[58],"for":[59,87,108,220,236,254],"bike-sharing":[60,177,255],"operators":[61],"to":[62,82,100,125,174,190,208,245],"optimize":[63],"inventory":[66],"location.":[69],"This":[70],"study":[71],"introduces":[72],"a":[73,180,184],"multi-step-ahead":[74],"forecasting":[75,192],"model":[76,173,196,242],"that":[77],"employs":[78],"machine":[79,144],"learning":[80,145],"methods":[81],"predict":[83],"hourly":[85],"demand":[86],"bikes.":[89],"We":[90,132],"utilize":[91],"information":[92],"on":[93,138],"from":[96,121],"preceding":[98],"day":[99],"forecast":[101],"forthcoming":[103],"counts":[104],"next":[110],"1,":[111,224],"3,":[112,225],"6,":[113,226],"12,":[114,227],"24":[116,229],"h.":[117],"Additional":[118],"features":[119],"extracted":[120],"timestamps":[122],"are":[123],"incorporated":[124],"enhance":[126],"accuracy":[128],"model.":[131],"compare":[133],"proposed":[135,171,195],"model,":[136],"based":[137],"multilayer":[139],"perceptron":[140],"(MLP),":[141],"with":[142,211],"various":[143],"prediction":[146,187,221],"algorithms,":[147],"including":[148],"Support":[149],"Vector":[150],"Regression":[151,167],"(SVR),":[152],"K-Nearest":[153],"Neighbor":[154],"(KNN),":[155],"Decision":[156],"Tree":[157],"(DT),":[158],"Adaptive":[159],"Boosting":[160],"(AdaBoost),":[161],"Random":[162],"Forest":[163],"(RF),":[164],"Linear":[166],"(LR).":[168],"Applying":[169],"MLP":[172],"Seoul":[176],"dataset":[178],"demonstrates":[179],"positive":[181],"outcome,":[182],"indicating":[183],"reduction":[185],"error":[188],"compared":[189,207],"other":[191,209],"models.":[193],"The":[194],"achieves":[197],"highest":[199],"R":[200],"2":[201],"(coefficient":[202],"determination)":[204],"values":[205,212,235],"when":[206],"models,":[210],"0.973,":[214],"0.882,":[215],"0.82,":[216],"0.807,":[217],"0.79":[219],"horizons":[222],"h,":[230],"respectively.":[231],"By":[232],"obtaining":[233],"predicted":[237],"bikes,":[239],"trained":[241],"anticipated":[244],"assist":[246],"optimizing":[248],"available":[252],"companies.":[256]},"counts_by_year":[],"updated_date":"2026-02-27T16:54:17.756197","created_date":"2026-01-08T00:00:00"}