{"id":"https://openalex.org/W2904500310","doi":"https://doi.org/10.31477/rjmf.201804.42","title":"Inflation Forecasting Using Machine Learning Methods","display_name":"Inflation Forecasting Using Machine Learning Methods","publication_year":2018,"publication_date":"2018-12-01","ids":{"openalex":"https://openalex.org/W2904500310","doi":"https://doi.org/10.31477/rjmf.201804.42","mag":"2904500310"},"language":"en","primary_location":{"id":"doi:10.31477/rjmf.201804.42","doi":"https://doi.org/10.31477/rjmf.201804.42","is_oa":true,"landing_page_url":"https://doi.org/10.31477/rjmf.201804.42","pdf_url":"https://rjmf.econs.online/upload/iblock/ca3/RJMF_77-04_ENG_Baybuza.pdf","source":{"id":"https://openalex.org/S4210237155","display_name":"Russian Journal of Money and Finance","issn_l":"2618-6799","issn":["2618-6799","2712-8369"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":["jufo-1"],"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":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Russian Journal of Money and Finance","raw_type":"journal-article"},"volume":null,"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://rjmf.econs.online/upload/iblock/ca3/RJMF_77-04_ENG_Baybuza.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5050736703","display_name":"Ivan Baybuza","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I8204097","display_name":"Ludwig-Maximilians-Universit\u00e4t M\u00fcnchen","ror":"https://ror.org/05591te55","country_code":"DE","type":"education","lineage":["https://openalex.org/I8204097"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Ivan Baybuza","raw_affiliation_strings":["University of Munich","Ludwig Maximilian University of Munich, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Munich","institution_ids":["https://openalex.org/I8204097"]},{"raw_affiliation_string":"Ludwig Maximilian University of Munich, Germany","institution_ids":["https://openalex.org/I8204097"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5050736703"],"corresponding_institution_ids":["https://openalex.org/I8204097"],"apc_list":null,"apc_paid":null,"fwci":1.5961,"has_fulltext":true,"cited_by_count":48,"citation_normalized_percentile":{"value":0.85485678,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"42","last_page":"59"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11918","display_name":"Forecasting Techniques and Applications","score":0.9818999767303467,"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/T11918","display_name":"Forecasting Techniques and Applications","score":0.9818999767303467,"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":0.01730000041425228,"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"}},{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.00039999998989515007,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/inflation-forecasting","display_name":"inflation forecasting","score":1.0},{"id":"https://openalex.org/keywords/machine-learning","display_name":"machine learning","score":0.7039999961853027},{"id":"https://openalex.org/keywords/random-forest","display_name":"random forest","score":0.9559999704360962},{"id":"https://openalex.org/keywords/boosting","display_name":"boosting","score":0.9900000095367432},{"id":"https://openalex.org/keywords/lasso","display_name":"LASSO","score":0.9120000004768372},{"id":"https://openalex.org/keywords/ridge-regression","display_name":"ridge regression","score":0.9929999709129333},{"id":"https://openalex.org/keywords/elastic-net","display_name":"elastic net","score":0.9959999918937683},{"id":"https://openalex.org/keywords/autoregression","display_name":"autoregression","score":0.8230000138282776},{"id":"https://openalex.org/keywords/random-walks","display_name":"random walks","score":0.8600000143051147},{"id":"https://openalex.org/keywords/forecast-accuracy","display_name":"forecast accuracy","score":0.8209999799728394}],"concepts":[{"id":"https://openalex.org/C200941418","wikidata":"https://www.wikidata.org/wiki/Q273508","display_name":"Inflation (cosmology)","level":2,"score":0.6972593069076538},{"id":"https://openalex.org/C37616216","wikidata":"https://www.wikidata.org/wiki/Q3218363","display_name":"Lasso (programming language)","level":2,"score":0.570417582988739},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.5655984282493591},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5512925386428833},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.5429224371910095},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5334480404853821},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.5301440954208374},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5116860866546631},{"id":"https://openalex.org/C203868755","wikidata":"https://www.wikidata.org/wiki/Q5353562","display_name":"Elastic net regularization","level":3,"score":0.4808155298233032},{"id":"https://openalex.org/C121194460","wikidata":"https://www.wikidata.org/wiki/Q856741","display_name":"Random walk","level":2,"score":0.45103535056114197},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.3981184959411621},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.1801324188709259},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.14517146348953247},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.09986507892608643},{"id":"https://openalex.org/C33332235","wikidata":"https://www.wikidata.org/wiki/Q18362","display_name":"Theoretical physics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.31477/rjmf.201804.42","doi":"https://doi.org/10.31477/rjmf.201804.42","is_oa":true,"landing_page_url":"https://doi.org/10.31477/rjmf.201804.42","pdf_url":"https://rjmf.econs.online/upload/iblock/ca3/RJMF_77-04_ENG_Baybuza.pdf","source":{"id":"https://openalex.org/S4210237155","display_name":"Russian Journal of Money and Finance","issn_l":"2618-6799","issn":["2618-6799","2712-8369"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":["jufo-1"],"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":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Russian Journal of Money and Finance","raw_type":"journal-article"},{"id":"pmh:oai:RePEc:bkr:journl:v:77:y:2018:i:4:p:42-59","doi":null,"is_oa":false,"landing_page_url":"https://rjmf.econs.online/upload/iblock/ca3/RJMF_77-04_ENG_Baybuza.pdf","pdf_url":"https://rjmf.econs.online/upload/iblock/ca3/RJMF_77-04_ENG_Baybuza.pdf","source":{"id":"https://openalex.org/S4306401271","display_name":"RePEc: Research Papers in Economics","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I77793887","host_organization_name":"Federal Reserve Bank of St. Louis","host_organization_lineage":["https://openalex.org/I77793887"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.31477/rjmf.201804.42","doi":"https://doi.org/10.31477/rjmf.201804.42","is_oa":true,"landing_page_url":"https://doi.org/10.31477/rjmf.201804.42","pdf_url":"https://rjmf.econs.online/upload/iblock/ca3/RJMF_77-04_ENG_Baybuza.pdf","source":{"id":"https://openalex.org/S4210237155","display_name":"Russian Journal of Money and Finance","issn_l":"2618-6799","issn":["2618-6799","2712-8369"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":["jufo-1"],"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":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Russian Journal of Money and Finance","raw_type":"journal-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2904500310.pdf","grobid_xml":"https://content.openalex.org/works/W2904500310.grobid-xml"},"referenced_works_count":10,"referenced_works":["https://openalex.org/W1503398984","https://openalex.org/W1528512439","https://openalex.org/W1546736513","https://openalex.org/W1678356000","https://openalex.org/W1686810756","https://openalex.org/W2118020653","https://openalex.org/W2607243734","https://openalex.org/W2749288741","https://openalex.org/W2906883195","https://openalex.org/W3121595883"],"related_works":["https://openalex.org/W7214034652","https://openalex.org/W7113494705","https://openalex.org/W3011565041","https://openalex.org/W4312988350","https://openalex.org/W4384343613","https://openalex.org/W7047434632","https://openalex.org/W4401428459","https://openalex.org/W4412956204","https://openalex.org/W7112017319","https://openalex.org/W3122533884"],"abstract_inverted_index":{"Inflation":[0],"forecasting":[1,58,83,109,135],"is":[2,70,126],"an":[3],"important":[4],"practical":[5],"problem.":[6],"This":[7,68],"paper":[8,69,125],"proposes":[9],"a":[10,44],"solution":[11],"to":[12,56,76,81],"this":[13,124],"problem":[14],"for":[15,43],"Russia":[16,139],"using":[17,140],"several":[18],"basic":[19],"machine":[20,78,141],"learning":[21,79,142],"methods:":[22],"LASSO,":[23],"Ridge,":[24],"Elastic":[25],"Net,":[26],"Random":[27,95,116],"Forest,":[28],"and":[29,63,98,118],"Boosting.":[30],"Despite":[31],"the":[32,40,52,57,73,82,94,99,127,130],"fact":[33],"that":[34,93],"these":[35],"methods":[36,80],"already":[37],"existed":[38],"in":[39,51,61,66,86,138],"early":[41],"2000s,":[42],"long":[45],"time":[46],"they":[47],"remained":[48],"almost":[49],"unnoticed":[50],"professional":[53],"literature":[54],"related":[55],"of":[59,72,84,123,129,132,136],"inflation":[60,65,85,108,137],"general,":[62],"Russian":[64],"particular.":[67],"one":[71],"first":[74],"attempts":[75],"apply":[77],"Russia.":[87],"The":[88,120],"present":[89],"empirical":[90],"study":[91],"demonstrates":[92],"Forest":[96],"model":[97,101],"Boosting":[100],"are":[102],"at":[103,107],"least":[104],"as":[105,110,115],"good":[106],"more":[111,133],"traditional":[112],"models,":[113],"such":[114],"Walk":[117],"autoregression.":[119],"main":[121],"result":[122],"confirmation":[128],"possibility":[131],"accurate":[134],"methods.":[143]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":10},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":17},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":6},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2026-10-16T09:59:22.235173","created_date":"2018-12-22T00:00:00"}