Main Article Content
Using a monthly time series of charcoal prices in Minas Gerais from January 2000 to September 2014, this study aimed to evaluate the use of neuro-fuzzy system to model the series and forecasting prices. We used four modeling structures for different prices lags (1, 2, 3, 4 and 5 lags). The structure most appropriate for neuro-fuzzy system was chosen based on the root mean square error, mean absolute error, mean squared error, mean absolute percentage error and maximum absolute percentage error for the forecasted period. With the results found, it is possible to conclude that a neuro-fuzzy system can be used properly to predict the charcoal prices.
How to Cite
ARAÚJO JÚNIOR, Carlos Alberto et al. MODELLING AND FORECAST OF CHARCOAL PRICES USING A NEURO-FUZZY SYSTEM. CERNE, [S.l.], v. 22, n. 2, p. 151-158, june 2016. ISSN 2317-6342. Available at: <http://cerne.ufla.br/site/index.php/CERNE/article/view/1199>. Date accessed: 22 sep. 2019.
Time series, Computational intelligence, ANFIS
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