{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T16:35:01Z","timestamp":1773246901912,"version":"3.50.1"},"reference-count":12,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,11,2]],"date-time":"2021-11-02T00:00:00Z","timestamp":1635811200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,11,2]],"date-time":"2021-11-02T00:00:00Z","timestamp":1635811200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,11,2]]},"DOI":"10.1109\/la-cci48322.2021.9769832","type":"proceedings-article","created":{"date-parts":[[2022,5,11]],"date-time":"2022-05-11T20:31:46Z","timestamp":1652301106000},"page":"1-6","source":"Crossref","is-referenced-by-count":5,"title":["Time Series Forecasting using NARX and NARMAX models with shallow and deep neural networks"],"prefix":"10.1109","author":[{"given":"Francisco","family":"Munoz","sequence":"first","affiliation":[{"name":"Universidad de Santiago de Chile,Departamento de Ingeniería Informática,Santiago,Chile"}]},{"given":"Gonzalo","family":"Acuna","sequence":"additional","affiliation":[{"name":"Universidad de Santiago de Chile,Departamento de Ingeniería Informática,Santiago,Chile"}]}],"member":"263","reference":[{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TLA.2016.7459644"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.fcij.2018.10.003"},{"key":"ref10","article-title":"Desarrollo de un Sensor Virtual basado en Modelo NARMAX y Máquina de Vectores de Soporte para Molienda Semiautógena","volume":"11","author":"acu\u00f1a","year":"2014","journal-title":"RIAI Revista Iberoamericana de Automática e Informática Industrial"},{"key":"ref6","article-title":"Chucking everything into a Random Forest: Ben Hamner on Winning The Air Quality Prediction Hackathon","author":"hamner","year":"2020","journal-title":"Kaggle Winner’s Blog"},{"key":"ref11","article-title":"sklearn.preprocessing.StandardScaler","year":"2020","journal-title":"scikit-learn 0 24 1"},{"key":"ref5","article-title":"EMC Data Science Global Hackathon (Air Quality Prediction)","year":"2012"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1016\/j.heliyon.2019.e01247"},{"key":"ref8","article-title":"statsmodels.tsa.stattools.adfuller","author":"perktold","year":"2019","journal-title":"Time series Analysis - User guide - statsmodels"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.chaos.2015.09.002"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-32-9019-8_14"},{"key":"ref9","article-title":"Multifractality in one dimensional distributions stat smodels tsa.stattools.adfuller","author":"rydin","year":"2020","journal-title":"MFDFA’s documentation"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijforecast.2006.01.001"}],"event":{"name":"2021 IEEE Latin American Conference on Computational Intelligence (LA-CCI)","location":"Temuco, Chile","start":{"date-parts":[[2021,11,2]]},"end":{"date-parts":[[2021,11,4]]}},"container-title":["2021 IEEE Latin American Conference on Computational Intelligence (LA-CCI)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9769800\/9769778\/09769832.pdf?arnumber=9769832","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,27]],"date-time":"2022-06-27T21:01:36Z","timestamp":1656363696000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9769832\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,2]]},"references-count":12,"URL":"https:\/\/doi.org\/10.1109\/la-cci48322.2021.9769832","relation":{},"subject":[],"published":{"date-parts":[[2021,11,2]]}}}