{"id":"https://openalex.org/W2097397325","doi":"https://doi.org/10.1109/conielecomp.2013.6525753","title":"Forecasting Mexican inflation using neural networks","display_name":"Forecasting Mexican inflation using neural networks","publication_year":2013,"publication_date":"2013-03-01","ids":{"openalex":"https://openalex.org/W2097397325","doi":"https://doi.org/10.1109/conielecomp.2013.6525753","mag":"2097397325"},"language":"en","primary_location":{"id":"doi:10.1109/conielecomp.2013.6525753","is_oa":false,"landing_page_url":"https://doi.org/10.1109/conielecomp.2013.6525753","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"CONIELECOMP 2013, 23rd International Conference on Electronics, Communications and Computing","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5111877900","display_name":"Jose Luis Chavez Hurtado","orcid":null},"institutions":[{"id":"https://openalex.org/I193181351","display_name":"Universidad de Guadalajara","ror":"https://ror.org/043xj7k26","country_code":"MX","type":"education","lineage":["https://openalex.org/I193181351"]}],"countries":["MX"],"is_corresponding":true,"raw_author_name":"Jose Luis Chavez Hurtado","raw_affiliation_strings":["Department of Quantitative Methods CUCEA, University of Guadalajara, Guadalajara, Mexico","Dept. of Quantitative Methods, Univ. of Guadalajara, Guadalajara, Mexico"],"affiliations":[{"raw_affiliation_string":"Department of Quantitative Methods CUCEA, University of Guadalajara, Guadalajara, Mexico","institution_ids":["https://openalex.org/I193181351"]},{"raw_affiliation_string":"Dept. of Quantitative Methods, Univ. of Guadalajara, Guadalajara, Mexico","institution_ids":["https://openalex.org/I193181351"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5096839818","display_name":"Jose Hector Cortes-Fregoso","orcid":null},"institutions":[{"id":"https://openalex.org/I193181351","display_name":"Universidad de Guadalajara","ror":"https://ror.org/043xj7k26","country_code":"MX","type":"education","lineage":["https://openalex.org/I193181351"]}],"countries":["MX"],"is_corresponding":false,"raw_author_name":"Jose Hector Cortes-Fregoso","raw_affiliation_strings":["Department of Quantitative Methods CUCEA, University of Guadalajara, Guadalajara, Mexico","Dept. of Quantitative Methods, Univ. of Guadalajara, Guadalajara, Mexico"],"affiliations":[{"raw_affiliation_string":"Department of Quantitative Methods CUCEA, University of Guadalajara, Guadalajara, Mexico","institution_ids":["https://openalex.org/I193181351"]},{"raw_affiliation_string":"Dept. of Quantitative Methods, Univ. of Guadalajara, Guadalajara, Mexico","institution_ids":["https://openalex.org/I193181351"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5111877900"],"corresponding_institution_ids":["https://openalex.org/I193181351"],"apc_list":null,"apc_paid":null,"fwci":0.4729,"has_fulltext":false,"cited_by_count":16,"citation_normalized_percentile":{"value":0.70636949,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"32","last_page":"35"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.9869999885559082,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.9869999885559082,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9864000082015991,"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/T11059","display_name":"Market Dynamics and Volatility","score":0.9638000130653381,"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/inflation","display_name":"Inflation (cosmology)","score":0.8320509195327759},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.7564078569412231},{"id":"https://openalex.org/keywords/economic-stability","display_name":"Economic stability","score":0.5173803567886353},{"id":"https://openalex.org/keywords/stability","display_name":"Stability (learning theory)","score":0.485927015542984},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.4824526906013489},{"id":"https://openalex.org/keywords/variable","display_name":"Variable (mathematics)","score":0.43868210911750793},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.42343568801879883},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4153205156326294},{"id":"https://openalex.org/keywords/work","display_name":"Work (physics)","score":0.4123358130455017},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.24926072359085083},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2360173463821411},{"id":"https://openalex.org/keywords/keynesian-economics","display_name":"Keynesian economics","score":0.22528409957885742},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.1523917019367218},{"id":"https://openalex.org/keywords/thermodynamics","display_name":"Thermodynamics","score":0.07190665602684021},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.06468412280082703}],"concepts":[{"id":"https://openalex.org/C200941418","wikidata":"https://www.wikidata.org/wiki/Q273508","display_name":"Inflation (cosmology)","level":2,"score":0.8320509195327759},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.7564078569412231},{"id":"https://openalex.org/C73374054","wikidata":"https://www.wikidata.org/wiki/Q2995667","display_name":"Economic stability","level":2,"score":0.5173803567886353},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.485927015542984},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.4824526906013489},{"id":"https://openalex.org/C182365436","wikidata":"https://www.wikidata.org/wiki/Q50701","display_name":"Variable (mathematics)","level":2,"score":0.43868210911750793},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.42343568801879883},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4153205156326294},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.4123358130455017},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.24926072359085083},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2360173463821411},{"id":"https://openalex.org/C165556158","wikidata":"https://www.wikidata.org/wiki/Q83937","display_name":"Keynesian economics","level":1,"score":0.22528409957885742},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.1523917019367218},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.07190665602684021},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.06468412280082703},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C33332235","wikidata":"https://www.wikidata.org/wiki/Q18362","display_name":"Theoretical physics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/conielecomp.2013.6525753","is_oa":false,"landing_page_url":"https://doi.org/10.1109/conielecomp.2013.6525753","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"CONIELECOMP 2013, 23rd International Conference on Electronics, Communications and Computing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/8","display_name":"Decent work and economic growth","score":0.5699999928474426}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":4,"referenced_works":["https://openalex.org/W2106245083","https://openalex.org/W2118704847","https://openalex.org/W2124776405","https://openalex.org/W3209493040"],"related_works":["https://openalex.org/W1512425385","https://openalex.org/W2052525980","https://openalex.org/W2384495009","https://openalex.org/W4384557449","https://openalex.org/W3125530942","https://openalex.org/W2983175216","https://openalex.org/W3114258117","https://openalex.org/W2184976113","https://openalex.org/W3113669155","https://openalex.org/W4317207348"],"abstract_inverted_index":{"In":[0],"this":[1,20],"work":[2],"we":[3],"use":[4],"a":[5,24,79,95,105,116,140],"Neural":[6,44,127],"Network":[7,45],"model":[8,72,119,129],"to":[9,33,68,135],"forecast":[10,15],"Mexican":[11,28],"inflation.":[12],"Related":[13],"works":[14],"inflation":[16,29,81,91,138],"in":[17,62],"countries":[18],"where":[19,83,98,108],"economic":[21],"variable":[22],"has":[23,30],"stable":[25],"behavior":[26],"while":[27],"been":[31],"characterized":[32],"be":[34],"very":[35],"volatile":[36,80],"during":[37,143],"certain":[38],"periods.":[39,146],"There":[40],"were":[41,74],"implemented":[42],"different":[43],"models":[46],"by":[47],"varying":[48],"the":[49,58,63,84,99,109,136],"number":[50,59],"of":[51,60,124],"hidden":[52,64],"layers":[53],"(1":[54],"and":[55,57,92,104,122],"2)":[56],"neurons":[61],"layer":[65],"(from":[66],"1":[67],"100).":[69],"The":[70],"forecasting":[71,120],"results":[73,121,130],"divided":[75],"into":[76],"3":[77],"categories:":[78],"phase,":[82,97,107],"mean":[85,100,110],"difference":[86,101,111],"was":[87,102,112],"0.64%":[88],"between":[89,118],"real":[90,137],"forecasted":[93],"inflation;":[94],"transition":[96],"5.44%;":[103],"stability":[106],"0.28%.":[113],"By":[114],"doing":[115],"comparison":[117],"Bank":[123],"Mexico's":[125],"predictions,":[126],"Networks":[128],"are":[131],"clearly":[132],"more":[133],"accurate":[134],"behavior,":[139],"critical":[141],"point":[142],"inflationary":[144],"crisis":[145]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":5},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":2},{"year":2017,"cited_by_count":1},{"year":2014,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}