{"id":"https://openalex.org/W4382999056","doi":"https://doi.org/10.1109/access.2023.3291999","title":"Prediction of COVID-19 Data Using Improved ARIMA-LSTM Hybrid Forecast Models","display_name":"Prediction of COVID-19 Data Using Improved ARIMA-LSTM Hybrid Forecast Models","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4382999056","doi":"https://doi.org/10.1109/access.2023.3291999"},"language":"en","primary_location":{"id":"doi:10.1109/access.2023.3291999","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2023.3291999","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10172007.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10172007.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5102872360","display_name":"Yongchao Jin","orcid":"https://orcid.org/0000-0002-9475-8300"},"institutions":[{"id":"https://openalex.org/I137506752","display_name":"North China University of Science and Technology","ror":"https://ror.org/04z4wmb81","country_code":"CN","type":"education","lineage":["https://openalex.org/I137506752"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yong-Chao Jin","raw_affiliation_strings":["College of Science, North China University of Science and Technology, Tangshan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Science, North China University of Science and Technology, Tangshan, China","institution_ids":["https://openalex.org/I137506752"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100836227","display_name":"Qian Cao","orcid":null},"institutions":[{"id":"https://openalex.org/I137506752","display_name":"North China University of Science and Technology","ror":"https://ror.org/04z4wmb81","country_code":"CN","type":"education","lineage":["https://openalex.org/I137506752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qian Cao","raw_affiliation_strings":["College of Science, North China University of Science and Technology, Tangshan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Science, North China University of Science and Technology, Tangshan, China","institution_ids":["https://openalex.org/I137506752"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002450740","display_name":"Kenan Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I137506752","display_name":"North China University of Science and Technology","ror":"https://ror.org/04z4wmb81","country_code":"CN","type":"education","lineage":["https://openalex.org/I137506752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ke-Nan Wang","raw_affiliation_strings":["College of Science, North China University of Science and Technology, Tangshan, China"],"raw_orcid":"https://orcid.org/0009-0007-3064-1756","affiliations":[{"raw_affiliation_string":"College of Science, North China University of Science and Technology, Tangshan, China","institution_ids":["https://openalex.org/I137506752"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100583732","display_name":"Yuan Zhou","orcid":null},"institutions":[{"id":"https://openalex.org/I137506752","display_name":"North China University of Science and Technology","ror":"https://ror.org/04z4wmb81","country_code":"CN","type":"education","lineage":["https://openalex.org/I137506752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuan Zhou","raw_affiliation_strings":["College of Artificial Intelligence, North China University of Science and Technology, Tangshan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Artificial Intelligence, North China University of Science and Technology, Tangshan, China","institution_ids":["https://openalex.org/I137506752"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5104077251","display_name":"Yanpeng Cao","orcid":null},"institutions":[{"id":"https://openalex.org/I137506752","display_name":"North China University of Science and Technology","ror":"https://ror.org/04z4wmb81","country_code":"CN","type":"education","lineage":["https://openalex.org/I137506752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yan-Peng Cao","raw_affiliation_strings":["College of Science, North China University of Science and Technology, Tangshan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Science, North China University of Science and Technology, Tangshan, China","institution_ids":["https://openalex.org/I137506752"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5068397432","display_name":"Xiyin Wang","orcid":"https://orcid.org/0000-0003-3454-0374"},"institutions":[{"id":"https://openalex.org/I137506752","display_name":"North China University of Science and Technology","ror":"https://ror.org/04z4wmb81","country_code":"CN","type":"education","lineage":["https://openalex.org/I137506752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xi-Yin Wang","raw_affiliation_strings":["College of Science, North China University of Science and Technology, Tangshan, China","Hebei Key Laboratory of Data Science and Application, Tangshan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Science, North China University of Science and Technology, Tangshan, China","institution_ids":["https://openalex.org/I137506752"]},{"raw_affiliation_string":"Hebei Key Laboratory of Data Science and Application, Tangshan, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5102872360"],"corresponding_institution_ids":["https://openalex.org/I137506752"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":6.0389,"has_fulltext":true,"cited_by_count":27,"citation_normalized_percentile":{"value":0.96965695,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"11","issue":null,"first_page":"67956","last_page":"67967"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11775","display_name":"COVID-19 diagnosis using AI","score":0.994700014591217,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T11775","display_name":"COVID-19 diagnosis using AI","score":0.994700014591217,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"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/T10410","display_name":"COVID-19 epidemiological studies","score":0.989300012588501,"subfield":{"id":"https://openalex.org/subfields/2611","display_name":"Modeling and Simulation"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9857000112533569,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/autoregressive-integrated-moving-average","display_name":"Autoregressive integrated moving average","score":0.8715066909790039},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.6852879524230957},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6483595967292786},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6357789039611816},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5914280414581299},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5407609343528748},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5386130809783936},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.483007550239563},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.41065728664398193},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3568447232246399},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.2588880658149719},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1494566798210144}],"concepts":[{"id":"https://openalex.org/C24338571","wikidata":"https://www.wikidata.org/wiki/Q2566298","display_name":"Autoregressive integrated moving average","level":3,"score":0.8715066909790039},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.6852879524230957},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6483595967292786},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6357789039611816},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5914280414581299},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5407609343528748},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5386130809783936},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.483007550239563},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.41065728664398193},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3568447232246399},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2588880658149719},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1494566798210144}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2023.3291999","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2023.3291999","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10172007.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:e62f64317ffb4ed4b7df5302adeb9db2","is_oa":true,"landing_page_url":"https://doaj.org/article/e62f64317ffb4ed4b7df5302adeb9db2","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":"IEEE Access, Vol 11, Pp 67956-67967 (2023)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2023.3291999","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2023.3291999","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10172007.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3","score":0.8399999737739563}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4382999056.pdf","grobid_xml":"https://content.openalex.org/works/W4382999056.grobid-xml"},"referenced_works_count":33,"referenced_works":["https://openalex.org/W1972732865","https://openalex.org/W2021000795","https://openalex.org/W2056646884","https://openalex.org/W2091249059","https://openalex.org/W2573137292","https://openalex.org/W2773797127","https://openalex.org/W2981112632","https://openalex.org/W2981592639","https://openalex.org/W2987621578","https://openalex.org/W2999869395","https://openalex.org/W3014995369","https://openalex.org/W3043618167","https://openalex.org/W3045889130","https://openalex.org/W3046555906","https://openalex.org/W3085456761","https://openalex.org/W3092118086","https://openalex.org/W3092443417","https://openalex.org/W3097257811","https://openalex.org/W3111464201","https://openalex.org/W3112226226","https://openalex.org/W3133226194","https://openalex.org/W3201535928","https://openalex.org/W3204737361","https://openalex.org/W3207313055","https://openalex.org/W3216016011","https://openalex.org/W4200418863","https://openalex.org/W4213005472","https://openalex.org/W4220917664","https://openalex.org/W4229058446","https://openalex.org/W4281610836","https://openalex.org/W4288427501","https://openalex.org/W4307765000","https://openalex.org/W4360615709"],"related_works":["https://openalex.org/W3080406149","https://openalex.org/W3175321409","https://openalex.org/W4312561791","https://openalex.org/W2389894046","https://openalex.org/W2215717369","https://openalex.org/W4391216528","https://openalex.org/W4312309719","https://openalex.org/W2980748541","https://openalex.org/W4399581288","https://openalex.org/W4313123484"],"abstract_inverted_index":{"COVID-19":[0,31],"has":[1,10,32],"developed":[2],"into":[3,114],"a":[4,34,203,319],"global":[5],"public":[6,300,310],"health":[7,301,332],"emergency":[8],"and":[9,39,47,85,123,134,139,148,157,166,180,190,219,240,245,256,274,299,307,314,322,333,338,347],"led":[11],"to":[12,57,90,206,226,284,329,351],"restrictions":[13],"in":[14,75,102,210,303],"numerous":[15],"nations.":[16],"Thousands":[17],"of":[18,25,27,60,105,132,159,248,335],"deaths":[19],"have":[20],"resulted":[21],"from":[22,238,356],"the":[23,43,58,82,92,97,103,130,154,173,183,191,235,242,249,278,309,331,354,357],"infection":[24],"millions":[26],"individuals":[28],"globally.":[29],"Additionally,":[30],"had":[33,182],"significant":[35],"impact":[36],"on":[37],"social":[38],"economic":[40],"activity":[41],"around":[42],"world.":[44],"The":[45,187,212,258,267],"elderly":[46],"those":[48],"with":[49],"existing":[50],"medical":[51,349],"issues,":[52],"however,":[53],"are":[54,71,168],"particularly":[55],"vulnerable":[56],"effects":[59],"COVID-19.":[61],"Pneumonia,":[62],"acute":[63],"respiratory":[64],"distress":[65],"syndrome,":[66],"organ":[67],"failure,":[68],"death,":[69],"etc.":[70],"all":[72],"possible":[73],"outcomes":[74],"severe":[76],"cases...":[77],"Traditional":[78],"prediction":[79,94,122,185,281],"approaches":[80],"like":[81],"ARIMA":[83,192,275],"model":[84,89,189,193,205,261,273,276],"multiple":[86,220],"linear":[87,93,221],"regression":[88,222],"handle":[91],"problem":[95],"because":[96],"new":[98],"crown":[99],"virus":[100],"is":[101,262],"process":[104],"continual":[106],"mutation.":[107],"Deep":[108],"learning":[109,136,150],"models":[110,138,288],"that":[111,172],"can":[112,145,296],"take":[113],"account":[115],"nonlinear":[116],"elements":[117],"include":[118],"BP":[119,213,268],"neural":[120,125,214,269],"network":[121,126,270],"LSTM":[124,188,272],"prediction.":[127],"To":[128,199,229],"combine":[129],"benefits":[131],"traditional":[133,147],"deep":[135,149],"predictive":[137,142,151],"create":[140],"superior":[141],"models,":[143,163],"we":[144,233],"blend":[146],"models.":[152],"When":[153],"MSE,":[155,178,243],"RMSE,":[156,179,244],"MAE":[158,246],"these":[160],"three":[161],"combined":[162],"PSO-LSTM-ARIMA,":[164],"MLR-LSTM-ARIMA,":[165],"BPNN-LSTM-ARIMA,":[167],"compared.":[169],"We":[170],"discovered":[171],"third":[174],"model,":[175,251],"which":[176,252,295],"included":[177],"MAE,":[181],"best":[184],"accuracy.":[186],"were":[194,223,253],"selected":[195],"for":[196],"this":[197,231,264],"investigation.":[198],"begin,":[200],"it":[201],"employed":[202],"single":[204],"forecast":[207],"pandemic":[208,312],"data":[209,237,291],"Germany.":[211],"network,":[215],"particle":[216],"swarm":[217],"method,":[218],"then":[224],"utilized":[225],"merge":[227],"it.":[228],"corroborate":[230],"finding,":[232],"re-predicted":[234],"epidemic":[236],"Japan":[239],"retrieved":[241],"values":[247],"BPNN-LSTM-ARIMA":[250],"6141895.956,":[254],"2478.285":[255],"1249.832.":[257],"most":[259],"accurate":[260,280],"still":[263],"integrated":[265],"model.":[266],"coupled":[271],"offers":[277],"highest":[279],"effect,":[282],"according":[283],"our":[285,293],"research.":[286],"Combinatorial":[287],"anticipate":[289],"outbreak":[290],"through":[292],"study,":[294],"aid":[297],"governments":[298],"authorities":[302],"improving":[304],"their":[305,336],"responses":[306],"educating":[308],"about":[311],"trends":[313],"potential":[315],"future":[316],"directions.":[317],"As":[318],"result,":[320],"industries":[321],"enterprises":[323],"may":[324],"make":[325],"better":[326,345,352],"risk-management":[327],"decisions":[328],"protect":[330],"safety":[334],"operations":[337],"personnel.":[339],"It":[340],"also":[341],"helps":[342],"healthcare":[343],"facilities":[344],"prepare":[346],"deploy":[348],"resources":[350],"meet":[353],"demands":[355],"pandemic.":[358]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":12},{"year":2024,"cited_by_count":12},{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
