The use of multilayer perceptrons for statistical modeling SO2 non linear time series in Salta Capital, Argentina
In this paper a statistical study of phisical-chemistry variables connected with enviroment pollution, specifically SO2 monthly average concentration, measured in Salta Capital city, Argentina, together with NO2 and O3 concentrations, was made. Time series under study shown non linear dinamic behavi...
| Autores: | , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2013 |
| País: | Costa Rica |
| Institución: | Universidad de Costa Rica |
| Repositorio: | Portal de Revistas UCR |
| Idioma: | español |
| OAI Identifier: | oai:portal.ucr.ac.cr:article/8479 |
| Acceso en línea: | https://revistas.ucr.ac.cr/index.php/matematica/article/view/8479 |
| Access Level: | acceso abierto |
| Palabra clave: | time series modelling multistep perceptrons air pollution sulfure dioxide passive sampling series de tiempo modelización perceptrones multicapa contaminación ambiental dióxido de azufre muestreo pasivo |
| Sumario: | In this paper a statistical study of phisical-chemistry variables connected with enviroment pollution, specifically SO2 monthly average concentration, measured in Salta Capital city, Argentina, together with NO2 and O3 concentrations, was made. Time series under study shown non linear dinamic behaviour, outliers and structural changes. Due to these it was impossible to use typical econometric typologies (AR, MA, ARMA, ARIMA, among others). Aneffective solution which uses multistep perceptrons theory was found. By using structural time series modelling, this solution is presentedby an iterative mathematical process that allows us to obtain a finalmodel with a high confidence level (95%) in order to do thefore casting step on the studied variable. |
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