¹Universidad Federal de Pernambuco, Brasil, Maestro en Estadística. E-mail: csantana@cable.net.co

Evaluar la capacidad de las redes neuronales en la predicción de series temporales es de sumo interés. Una aplicación que pronostique valores futu ros de la serie de inflación colombiana permite mostrar que las redes neuro nales pueden ser más precisas que las metodologías SARIMA de Box-Jenkins y el suavizamiento exponencial. Además, los resultados revelan que la combi nación de pronósticos que hacen uso de las redes neuronales tiende a mejorar la capacidad de predicción.

Palabras Claves: Perceptron multicapas, modelos SARIMA, suavizamiento exponencial, combinación de pronósticos, componentes no observables.

Evaluating the usefulness of neural network methods in predicting the Colombian Inflation is the main goal of this paper. The results show that neural networks forecasts can be considerably more accurate than forecasts obtained using exponential smoothing and SARIMA methods. Experimental results also show that combinations of individual neural networks forecasts improves the forecasting accuracy.

Key words: Multilayer perceptron, SARIMA models, Exponencial smooth- ing, Combination of forecasts, Unobservable components.

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