Forecasting Time Series with Neural Networks: An Application to the Colombian Inflation

Juan Camilo Santana

 

 

Abstract   

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 smoothing, Combination of forecasts, Unobservable components.

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