Abstract
This paper presents a twelve-month forecast of copper price time series developed by means of Generalized regression neural networks with optimized predictor variables. To achieve this goal, in first place the optimum size of the lagged variable was estimated by trial and error method. Second, the order in the time series of the lagged variables was considered and introduced in the predictor variable. A combination of metrics using the Root mean squared error, the Mean absolute error as well as the Standard deviation of absolute error, were selected as figures of merit. Training results clearly state that both optimizations allow improving the forecasting performance.
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Valverde, G.F., Krzemień, A., Fernández, P.R., Rodríguez, F.J.I., Sánchez, A.S. (2021). Copper Price Time Series Forecasting by Means of Generalized Regression Neural Networks with Optimized Predictor Variables. In: Herrero, Á., Cambra, C., Urda, D., Sedano, J., Quintián, H., Corchado, E. (eds) 15th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2020). SOCO 2020. Advances in Intelligent Systems and Computing, vol 1268. Springer, Cham. https://doi.org/10.1007/978-3-030-57802-2_65
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DOI: https://doi.org/10.1007/978-3-030-57802-2_65
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