A neural network approach to study o3 and PM10 concentration in environmental pollution
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
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The objective of this paper is to apply time series analysis to Ozone data in order to obtain the optimal forecasting model . Different ARMA models are fitted to the Ozone data and the best fitted model, ARMA(20,2), is found to produce the best predictions withMAPE = 42%. Applying simple exponential smoothing to the time series, however, results in even higher accuracy for predictions. This leads us to believe that in certain cases depending on the characteristics of the time series, naïve methods of forecasting may produce more accurate results.