Forecasting \(\text{SO}_{2}\) pollution incidents by means of Elman artificial neural networks and ARIMA models

DOI10.1155/2013/238259zbMath1314.91170OpenAlexW1989272293WikidataQ57838071 ScholiaQ57838071MaRDI QIDQ2015248

Celestino Ordóñez, Francisco Javier De Cos Juez, Antonio Bernardo Sánchez, Fernando Sánchez Lasheras, Javier Roca‐Pardiñas

Publication date: 23 June 2014

Published in: Abstract and Applied Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1155/2013/238259

zbMATH Keywords

ARIMA model\(\text{SO}_{2}\) emissionElman artificial neural network

Mathematics Subject Classification ID

Economic time series analysis (91B84) Learning and adaptive systems in artificial intelligence (68T05) Environmental economics (natural resource models, harvesting, pollution, etc.) (91B76)

Uses Software

• forecast
• Forecast

Cites Work

• Unnamed Item
• Time series forecasting using a hybrid ARIMA and neural network model
• Model identification of ARIMA family using genetic algorithms
• On a measure of lack of fit in time series models

This page was built for publication: Forecasting \(\text{SO}_{2}\) pollution incidents by means of Elman artificial neural networks and ARIMA models