{"id":"https://openalex.org/W7140797174","doi":"https://doi.org/10.6339/26-jds1223","title":"Predicting Business Cycles Using Deep Learning Models","display_name":"Predicting Business Cycles Using Deep Learning Models","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7140797174","doi":"https://doi.org/10.6339/26-jds1223"},"language":"en","primary_location":{"id":"doi:10.6339/26-jds1223","doi":"https://doi.org/10.6339/26-jds1223","is_oa":true,"landing_page_url":"https://doi.org/10.6339/26-jds1223","pdf_url":"https://jds-online.org/journal/JDS/article/1482/file/pdf","source":{"id":"https://openalex.org/S2764553900","display_name":"Journal of Data Science","issn_l":"1680-743X","issn":["1680-743X","1683-8602"],"is_oa":true,"is_in_doaj":true,"is_core":false,"listed_in":["anvur-scientific","doaj","norway-1"],"host_organization":"https://openalex.org/P4327988825","host_organization_name":"People's University of China","host_organization_lineage":["https://openalex.org/P4327988825"],"host_organization_lineage_names":["People's University of China"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Data Science","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://jds-online.org/journal/JDS/article/1482/file/pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5006598728","display_name":"Binod Rimal","orcid":"https://orcid.org/0000-0001-5253-6972","observed_orcids":["https://orcid.org/0000-0001-5253-6972"]},"institutions":[{"id":"https://openalex.org/I93320256","display_name":"University of Tampa","ror":"https://ror.org/007h1g065","country_code":"US","type":"education","lineage":["https://openalex.org/I93320256"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Binod Rimal","raw_affiliation_strings":["1 401 W Kennedy Blvd , Tampa , FL , 33606 , University of Tampa , USA"],"raw_orcid":"https://orcid.org/0000-0001-5253-6972","affiliations":[{"raw_affiliation_string":"1 401 W Kennedy Blvd , Tampa , FL , 33606 , University of Tampa , USA","institution_ids":["https://openalex.org/I93320256"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130691860","display_name":"Sebastian Tardieu","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I93320256","display_name":"University of Tampa","ror":"https://ror.org/007h1g065","country_code":"US","type":"education","lineage":["https://openalex.org/I93320256"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sebastian Tardieu","raw_affiliation_strings":["1 401 W Kennedy Blvd , Tampa , FL , 33606 , University of Tampa , USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"1 401 W Kennedy Blvd , Tampa , FL , 33606 , University of Tampa , USA","institution_ids":["https://openalex.org/I93320256"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067210481","display_name":"Ramchandra Rimal","orcid":"https://orcid.org/0000-0001-8182-0593","observed_orcids":["https://orcid.org/0000-0001-8182-0593"]},"institutions":[{"id":"https://openalex.org/I169615421","display_name":"Middle Tennessee State University","ror":"https://ror.org/02n1hzn07","country_code":"US","type":"education","lineage":["https://openalex.org/I169615421"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ramchandra Rimal","raw_affiliation_strings":["1301 E Main St , Murfreesboro , TN , Middle Tennessee State University , USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"1301 E Main St , Murfreesboro , TN , Middle Tennessee State University , USA","institution_ids":["https://openalex.org/I169615421"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5005688272","display_name":"Hum Nath Bhandari","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hum\u00a0Nath Bhandari","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"20"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.6815999746322632,"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":0.6815999746322632,"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/T11918","display_name":"Forecasting Techniques and Applications","score":0.31150001287460327,"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/T10007","display_name":"Monetary Policy and Economic Impact","score":0.0020000000949949026,"subfield":{"id":"https://openalex.org/subfields/2000","display_name":"General Economics, Econometrics and Finance"},"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/business-cycle-forecasting","display_name":"business cycle forecasting","score":0.9589999914169312},{"id":"https://openalex.org/keywords/lstm","display_name":"LSTM","score":0.5649999976158142},{"id":"https://openalex.org/keywords/gated-recurrent-unit","display_name":"gated recurrent unit","score":0.8579999804496765},{"id":"https://openalex.org/keywords/deep-learning","display_name":"deep learning","score":0.597000002861023},{"id":"https://openalex.org/keywords/input-feature-selection","display_name":"input feature selection","score":0.7129999995231628},{"id":"https://openalex.org/keywords/hyperparameter-optimization","display_name":"hyperparameter optimization","score":0.9950000047683716},{"id":"https://openalex.org/keywords/model-selection","display_name":"model selection","score":0.8159999847412109},{"id":"https://openalex.org/keywords/economic-sectors","display_name":"economic sectors","score":0.41600000858306885}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7777000069618225},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7348999977111816},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.7177000045776367},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6593000292778015},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.5809999704360962},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.5738000273704529},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.5115000009536743},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.5073999762535095},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.45350000262260437},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.43790000677108765},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.42640000581741333},{"id":"https://openalex.org/C83873408","wikidata":"https://www.wikidata.org/wiki/Q192311","display_name":"Business cycle","level":2,"score":0.4259999990463257},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.41200000047683716},{"id":"https://openalex.org/C10551718","wikidata":"https://www.wikidata.org/wiki/Q5227332","display_name":"Data pre-processing","level":2,"score":0.4000999927520752},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.3763999938964844},{"id":"https://openalex.org/C100660578","wikidata":"https://www.wikidata.org/wiki/Q18733","display_name":"Recall","level":2,"score":0.37279999256134033},{"id":"https://openalex.org/C66024118","wikidata":"https://www.wikidata.org/wiki/Q1122506","display_name":"Computational model","level":2,"score":0.3497999906539917},{"id":"https://openalex.org/C2777432860","wikidata":"https://www.wikidata.org/wiki/Q6510168","display_name":"Learning cycle","level":2,"score":0.3361999988555908},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.32510000467300415},{"id":"https://openalex.org/C127705205","wikidata":"https://www.wikidata.org/wiki/Q5748245","display_name":"Heuristics","level":2,"score":0.3197999894618988},{"id":"https://openalex.org/C163068380","wikidata":"https://www.wikidata.org/wiki/Q3409313","display_name":"Economic forecasting","level":2,"score":0.29260000586509705},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.2913999855518341},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.2856999933719635},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.2854999899864197}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.6339/26-jds1223","doi":"https://doi.org/10.6339/26-jds1223","is_oa":true,"landing_page_url":"https://doi.org/10.6339/26-jds1223","pdf_url":"https://jds-online.org/journal/JDS/article/1482/file/pdf","source":{"id":"https://openalex.org/S2764553900","display_name":"Journal of Data Science","issn_l":"1680-743X","issn":["1680-743X","1683-8602"],"is_oa":true,"is_in_doaj":true,"is_core":false,"listed_in":["anvur-scientific","doaj","norway-1"],"host_organization":"https://openalex.org/P4327988825","host_organization_name":"People's University of China","host_organization_lineage":["https://openalex.org/P4327988825"],"host_organization_lineage_names":["People's University of China"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Data Science","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.6339/26-jds1223","doi":"https://doi.org/10.6339/26-jds1223","is_oa":true,"landing_page_url":"https://doi.org/10.6339/26-jds1223","pdf_url":"https://jds-online.org/journal/JDS/article/1482/file/pdf","source":{"id":"https://openalex.org/S2764553900","display_name":"Journal of Data Science","issn_l":"1680-743X","issn":["1680-743X","1683-8602"],"is_oa":true,"is_in_doaj":true,"is_core":false,"listed_in":["anvur-scientific","doaj","norway-1"],"host_organization":"https://openalex.org/P4327988825","host_organization_name":"People's University of China","host_organization_lineage":["https://openalex.org/P4327988825"],"host_organization_lineage_names":["People's University of China"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Data Science","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/W7140797174.pdf","grobid_xml":"https://content.openalex.org/works/W7140797174.grobid-xml"},"referenced_works_count":45,"referenced_works":["https://openalex.org/W646527304","https://openalex.org/W1741204264","https://openalex.org/W2005173315","https://openalex.org/W2043030203","https://openalex.org/W2052489773","https://openalex.org/W2064675550","https://openalex.org/W2130124677","https://openalex.org/W2146207816","https://openalex.org/W2198606573","https://openalex.org/W2224090338","https://openalex.org/W2257263437","https://openalex.org/W2302027039","https://openalex.org/W2337441499","https://openalex.org/W2510889454","https://openalex.org/W2531204024","https://openalex.org/W2581082771","https://openalex.org/W2766447205","https://openalex.org/W2811103148","https://openalex.org/W2890989484","https://openalex.org/W2955639054","https://openalex.org/W2966300525","https://openalex.org/W2979358647","https://openalex.org/W2996608372","https://openalex.org/W3007066689","https://openalex.org/W3007608152","https://openalex.org/W3089990971","https://openalex.org/W3093186795","https://openalex.org/W3120552598","https://openalex.org/W3121220818","https://openalex.org/W3162387763","https://openalex.org/W3172117295","https://openalex.org/W3175677706","https://openalex.org/W3178454468","https://openalex.org/W3189001139","https://openalex.org/W4200178216","https://openalex.org/W4287511819","https://openalex.org/W4289341905","https://openalex.org/W4292168395","https://openalex.org/W4319791194","https://openalex.org/W4367154886","https://openalex.org/W4384436107","https://openalex.org/W4391140332","https://openalex.org/W4392927912","https://openalex.org/W4399332106","https://openalex.org/W4400403004"],"related_works":["https://openalex.org/W3188179300","https://openalex.org/W7213299602","https://openalex.org/W4410641076","https://openalex.org/W4399116425","https://openalex.org/W4412822327","https://openalex.org/W7165585644","https://openalex.org/W4391392376","https://openalex.org/W4417444800","https://openalex.org/W4410267237","https://openalex.org/W4412366256"],"abstract_inverted_index":{"Forecasting":[0],"business":[1,64,91],"cycles":[2],"and":[3,13,18,43,57,123,133,151,157,196,214],"macroeconomic":[4,171],"trends":[5],"is":[6],"inherently":[7],"challenging":[8],"due":[9],"to":[10,39,63,130],"their":[11,61],"complex":[12,58],"non-linear":[14],"relationships":[15],"with":[16,31,178,185],"volatile":[17],"noisy":[19,56,116],"economic":[20,28,110],"factors.":[21],"However,":[22],"the":[23,118,134,163,166,175,182,202],"growing":[24],"availability":[25],"of":[26,97,109,120,136,165,188,191,194,199],"large-scale":[27],"data,":[29,117],"coupled":[30],"advances":[32],"in":[33],"computational":[34,81,125],"power,":[35],"creates":[36],"new":[37],"opportunities":[38],"extract":[40],"meaningful":[41],"information":[42],"develop":[44],"robust":[45,137],"predictive":[46,160],"models.":[47],"Deep":[48],"learning":[49,87],"methods":[50],"are":[51],"well":[52],"suited":[53],"for":[54,89,115,169],"handling":[55],"data;":[59],"nevertheless,":[60],"application":[62],"cycle":[65,92],"prediction":[66],"remains":[67],"at":[68],"an":[69,79,186,197],"early":[70],"stage.":[71],"To":[72],"address":[73],"this":[74,76,98],"gap,":[75],"study":[77],"develops":[78],"end-to-end":[80],"framework":[82],"that":[83,207],"implements":[84],"state-of-art":[85],"deep":[86],"architectures":[88],"identifying":[90],"phases.":[93],"The":[94],"key":[95],"contributions":[96],"work":[99],"include":[100],"systematic":[101],"input":[102],"feature":[103],"selection":[104,139],"across":[105],"a":[106,121,127],"broad":[107],"range":[108],"sectors,":[111],"advanced":[112],"preprocessing":[113],"techniques":[114],"development":[119],"customizable":[122],"reproducible":[124],"framework,":[126],"data-driven":[128],"approach":[129,168],"hyperparameter":[131],"tuning,":[132],"use":[135],"model":[138,177],"strategies.":[140],"Twenty":[141],"two":[142,146],"models":[143],"based":[144],"on":[145],"architectures\u2014Long":[147],"Short-Term":[148],"Memory":[149],"(LSTM)":[150],"Gated":[152],"Recurrent":[153],"Unit":[154],"(GRU)\u2014were":[155],"implemented":[156],"demonstrated":[158],"strong":[159],"performance,":[161],"highlighting":[162],"effectiveness":[164],"proposed":[167],"forecasting":[170],"trends.":[172],"Among":[173],"these,":[174],"GRU":[176],"512":[179],"neurons":[180],"achieved":[181],"best":[183],"results,":[184],"accuracy":[187],"86.42%,":[189],"precision":[190],"92.03%,":[192],"recall":[193],"93.32%,":[195],"F1-score":[198],"92.34%.":[200],"Overall,":[201],"findings":[203],"provide":[204],"valuable":[205],"insights":[206],"can":[208],"support":[209],"informed":[210],"decision-making":[211],"by":[212],"policymakers":[213],"other":[215],"stakeholders.":[216]},"counts_by_year":[],"updated_date":"2026-11-10T11:39:43.021295","created_date":"2026-03-27T00:00:00"}