SELECTION OF THE TRAINING ALGORITHMS FOR THE ARTIFICIAL NEURAL NETWORK
TO PREDICT THE TIME SERIES OF THE METHANE AND CARBON DIOXIDE
CONCENTRATIONS
Abstract
This paper presents a study and comparison of the most used learning
algorithms for nonlinear autoregressive neural network with external
input (NARX). These networks are successfully used to predict the time
series. For this study, the data of methane (CH4) and carbon dioxide
(CO2) concentrations in the surface layer of atmospheric air on the
Arctic island Belyy, Russia were used. A time interval of 190 hours was
chosen with the one-hour lag. Methane and carbon dioxide concentrations
corresponding to the first 170 hours of the interval were used for train
the NARX network. Then the forecast was made for the next 20 hours.
Three techniques were used as the learning algorithms:
Levenberg-Marquart (LM), LM with Bayesian regularization (BR), and
gradient descent with adjustable speed parameters (GDA). The NARX model,
which using the LM learning algorithm, was the most accurate for the
both greenhouse gases. The application of this learning algorithm
improved the predictive accuracy of the models from 9% to 12% for the
methane and from 7% to 21% for carbon monoxide. The predictive
problems in the field of dynamics of changes in the concentration of
greenhouse gases can be effectively solved using artificial neural
networks, in particular, NARX with the Levenberg-Marquart learning
algorithm.