{"id":1339,"date":"2025-05-19T14:49:54","date_gmt":"2025-05-19T14:49:54","guid":{"rendered":"https:\/\/face.unt.edu.ar\/web\/ieconomia\/?p=1339"},"modified":"2025-05-19T14:52:06","modified_gmt":"2025-05-19T14:52:06","slug":"prediccion-del-dolar-blue-en-argentina-big-data-y-machine-learning-para-anticipar-el-mercado","status":"publish","type":"post","link":"https:\/\/face.unt.edu.ar\/web\/ieconomia\/prediccion-del-dolar-blue-en-argentina-big-data-y-machine-learning-para-anticipar-el-mercado\/","title":{"rendered":"Predicci\u00f3n del D\u00f3lar Blue en Argentina: Big Data y Machine Learning para Anticipar el Mercado"},"content":{"rendered":"<p>&nbsp;<\/p>\n<p>En Argentina, el comportamiento del d\u00f3lar blue, profundamente influenciado por la<br \/>\npercepci\u00f3n y el inter\u00e9s p\u00fablico, se convierte en el foco de esta investigaci\u00f3n, que integra Big<br \/>\nData y Machine Learning para predecir sus oscilaciones. Mediante el uso del modelo<br \/>\nLightGBM y Google Trends, este estudio intenta determinar c\u00f3mo el inter\u00e9s espec\u00edfico en<br \/>\nciertos t\u00e9rminos puede anticipar las variaciones en el precio del d\u00f3lar blue. Los resultados<br \/>\nrevelan que, si bien la inclusi\u00f3n de variables de inter\u00e9s general de Google Trends mejora la<br \/>\ncapacidad del modelo para identificar tendencias alcistas (precisi\u00f3n = 72%), el costo es<br \/>\nuna notable incapacidad para predecir adecuadamente las ca\u00eddas en el precio del d\u00f3lar.<\/p>\n<p>&nbsp;<\/p>\n<p>Autores: Mat\u00edas Jos\u00e9 Nougu\u00e9s y Juan Benjam\u00edn Agusti<\/p>\n<p>Director: Santiago Foguet<\/p>\n<p><a href=\"https:\/\/drive.google.com\/file\/d\/1ErCybohpueI8SC10uoQ720ks6pF15A8m\/view?usp=sharing\" target=\"_blank\" rel=\"noopener\">Lee la tesis completa haciendo click aqu\u00ed<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>&nbsp; En Argentina, el comportamiento del d\u00f3lar blue, profundamente influenciado por la percepci\u00f3n y el inter\u00e9s p\u00fablico, se convierte en el foco de esta investigaci\u00f3n, que integra Big Data y Machine Learning para predecir sus oscilaciones. Mediante el uso del modelo LightGBM y Google Trends, este estudio intenta determinar c\u00f3mo el inter\u00e9s espec\u00edfico en ciertos [&hellip;]<\/p>\n","protected":false},"author":123458,"featured_media":1340,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1,8],"tags":[],"_links":{"self":[{"href":"https:\/\/face.unt.edu.ar\/web\/ieconomia\/wp-json\/wp\/v2\/posts\/1339"}],"collection":[{"href":"https:\/\/face.unt.edu.ar\/web\/ieconomia\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/face.unt.edu.ar\/web\/ieconomia\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/face.unt.edu.ar\/web\/ieconomia\/wp-json\/wp\/v2\/users\/123458"}],"replies":[{"embeddable":true,"href":"https:\/\/face.unt.edu.ar\/web\/ieconomia\/wp-json\/wp\/v2\/comments?post=1339"}],"version-history":[{"count":3,"href":"https:\/\/face.unt.edu.ar\/web\/ieconomia\/wp-json\/wp\/v2\/posts\/1339\/revisions"}],"predecessor-version":[{"id":1343,"href":"https:\/\/face.unt.edu.ar\/web\/ieconomia\/wp-json\/wp\/v2\/posts\/1339\/revisions\/1343"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/face.unt.edu.ar\/web\/ieconomia\/wp-json\/wp\/v2\/media\/1340"}],"wp:attachment":[{"href":"https:\/\/face.unt.edu.ar\/web\/ieconomia\/wp-json\/wp\/v2\/media?parent=1339"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/face.unt.edu.ar\/web\/ieconomia\/wp-json\/wp\/v2\/categories?post=1339"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/face.unt.edu.ar\/web\/ieconomia\/wp-json\/wp\/v2\/tags?post=1339"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}