{"id":"https://openalex.org/W4408338961","doi":"https://doi.org/10.1177/14727978251321956","title":"Application of ARIMA model in classification and prediction of athlete training data","display_name":"Application of ARIMA model in classification and prediction of athlete training data","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4408338961","doi":"https://doi.org/10.1177/14727978251321956"},"language":"en","primary_location":{"id":"doi:10.1177/14727978251321956","is_oa":true,"landing_page_url":"https://doi.org/10.1177/14727978251321956","pdf_url":"https://journals.sagepub.com/doi/pdf/10.1177/14727978251321956","source":{"id":"https://openalex.org/S2765058733","display_name":"Journal of Computational Methods in Sciences and Engineering","issn_l":"1472-7978","issn":["1472-7978","1875-8983"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318577","host_organization_name":"IOS Press","host_organization_lineage":["https://openalex.org/P4310318577"],"host_organization_lineage_names":["IOS Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Computational Methods in Sciences and Engineering","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"bronze","oa_url":"https://journals.sagepub.com/doi/pdf/10.1177/14727978251321956","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5103175239","display_name":"Zhi Tang","orcid":"https://orcid.org/0000-0002-6021-8357"},"institutions":[{"id":"https://openalex.org/I24407930","display_name":"Hunan University of Science and Engineering","ror":"https://ror.org/04ymz0q33","country_code":"CN","type":"education","lineage":["https://openalex.org/I24407930"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zhi Tang","raw_affiliation_strings":["Hunan University of Science and Engineering","College of Sports, Hunan University of Science and Engineering, Yongzhou, China"],"affiliations":[{"raw_affiliation_string":"Hunan University of Science and Engineering","institution_ids":["https://openalex.org/I24407930"]},{"raw_affiliation_string":"College of Sports, Hunan University of Science and Engineering, Yongzhou, China","institution_ids":["https://openalex.org/I24407930"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5103175239"],"corresponding_institution_ids":["https://openalex.org/I24407930"],"apc_list":null,"apc_paid":null,"fwci":1.5743,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.78195624,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":"25","issue":"1","first_page":"850","last_page":"862"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9941999912261963,"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"}},"topics":[{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9941999912261963,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9883999824523926,"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/T11674","display_name":"Sports Analytics and Performance","score":0.9329000115394592,"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/autoregressive-integrated-moving-average","display_name":"Autoregressive integrated moving average","score":0.7073265314102173},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6389010548591614},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.5917572975158691},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.5196022391319275},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.46832898259162903},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4153059124946594},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.22651314735412598},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.08576983213424683},{"id":"https://openalex.org/keywords/meteorology","display_name":"Meteorology","score":0.06131243705749512}],"concepts":[{"id":"https://openalex.org/C24338571","wikidata":"https://www.wikidata.org/wiki/Q2566298","display_name":"Autoregressive integrated moving average","level":3,"score":0.7073265314102173},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6389010548591614},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.5917572975158691},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.5196022391319275},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46832898259162903},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4153059124946594},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.22651314735412598},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.08576983213424683},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.06131243705749512}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1177/14727978251321956","is_oa":true,"landing_page_url":"https://doi.org/10.1177/14727978251321956","pdf_url":"https://journals.sagepub.com/doi/pdf/10.1177/14727978251321956","source":{"id":"https://openalex.org/S2765058733","display_name":"Journal of Computational Methods in Sciences and Engineering","issn_l":"1472-7978","issn":["1472-7978","1875-8983"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318577","host_organization_name":"IOS Press","host_organization_lineage":["https://openalex.org/P4310318577"],"host_organization_lineage_names":["IOS Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Computational Methods in Sciences and Engineering","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1177/14727978251321956","is_oa":true,"landing_page_url":"https://doi.org/10.1177/14727978251321956","pdf_url":"https://journals.sagepub.com/doi/pdf/10.1177/14727978251321956","source":{"id":"https://openalex.org/S2765058733","display_name":"Journal of Computational Methods in Sciences and Engineering","issn_l":"1472-7978","issn":["1472-7978","1875-8983"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318577","host_organization_name":"IOS Press","host_organization_lineage":["https://openalex.org/P4310318577"],"host_organization_lineage_names":["IOS Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Computational Methods in Sciences and Engineering","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4408338961.pdf"},"referenced_works_count":28,"referenced_works":["https://openalex.org/W2754846726","https://openalex.org/W2905810270","https://openalex.org/W2920972701","https://openalex.org/W2944992868","https://openalex.org/W2946223265","https://openalex.org/W2949230211","https://openalex.org/W2961651406","https://openalex.org/W2963060833","https://openalex.org/W2975181469","https://openalex.org/W2982511588","https://openalex.org/W2998204279","https://openalex.org/W3000596148","https://openalex.org/W3034536504","https://openalex.org/W3045889130","https://openalex.org/W3082085270","https://openalex.org/W3084580190","https://openalex.org/W3089857198","https://openalex.org/W3091186462","https://openalex.org/W3136156967","https://openalex.org/W3140311490","https://openalex.org/W3180895085","https://openalex.org/W3195263692","https://openalex.org/W4200408085","https://openalex.org/W4210507064","https://openalex.org/W4223897441","https://openalex.org/W4233752196","https://openalex.org/W4312295667","https://openalex.org/W4387398830"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W4387369504","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"],"abstract_inverted_index":{"Traditional":[0],"athlete":[1,47,106,136,207],"training":[2,48,76,107,137,208],"data":[3,37,49,71,158,209],"classification":[4,26,159,172,202],"and":[5,16,27,45,53,56,72,77,84,102,129,132,134,145,169,203],"prediction":[6,184,204],"models":[7,200],"have":[8],"low":[9],"accuracy,":[10],"poor":[11],"processing":[12],"of":[13,87,105,147,157,179,186,198,205],"high-dimensional":[14],"data,":[15,138],"weak":[17],"dynamic":[18],"adaptability.":[19],"Before":[20],"applying":[21],"the":[22,29,69,82,88,100,114,143,148,155,164,170,183,187,196,206],"ARIMA":[23,115,161,188,199],"model":[24,116,122,162,189],"for":[25],"prediction,":[28],"first":[30],"step":[31,65,92,111],"is":[32,66,93,163,174,210],"to":[33,40,67,80,94,98,141,201],"use":[34],"an":[35],"automated":[36],"acquisition":[38],"system":[39],"connect":[41],"sports":[42],"devices,":[43],"collect":[44],"clean":[46],"in":[50],"real":[51],"time,":[52],"securely":[54],"store":[55],"backup":[57],"it":[58,74],"on":[59],"a":[60],"central":[61],"server.":[62],"The":[63,90,109,150],"second":[64],"preprocess":[68],"collected":[70],"divide":[73],"into":[75],"test":[78],"sets":[79],"ensure":[81],"accuracy":[83,144,156,173],"generalization":[85],"ability":[86],"model.":[89,149],"third":[91],"conduct":[95],"time-series":[96],"analysis":[97],"identify":[99],"time-dependent":[101],"seasonal":[103],"components":[104],"data.":[108],"fourth":[110],"involves":[112],"fitting":[113],"through":[117],"differential":[118],"analysis,":[119,126],"stationarity":[120],"testing,":[121],"parameter":[123],"optimization,":[124],"residual":[125],"rolling":[127],"forecasting,":[128],"ensemble":[130],"learning,":[131],"predicting":[133],"classifying":[135],"so":[139],"as":[140],"improve":[142],"robustness":[146],"experimental":[151],"results":[152],"show":[153],"that":[154,178],"using":[160],"highest,":[165],"all":[166,191],"exceeding":[167],"92%,":[168],"average":[171],"2%\u201316.7%":[175],"higher":[176],"than":[177],"other":[180],"models.":[181],"Moreover,":[182],"errors":[185],"are":[190],"below":[192],"1.0%.":[193],"In":[194],"summary,":[195],"application":[197],"highly":[211],"reliable.":[212]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2025-12-28T23:10:05.387466","created_date":"2025-10-10T00:00:00"}
