Avances Metodológicos en Redes Neuronales Artificiales Recientes para el Pronostico de Series de Tiempo (Methodological Advances in Artificial Neural Networks for Time Series Forecasting)

Myladis Rocio Cogollo (mcogollo@eafit.edu.co)1, Juan David Velásquez (jdvelasq@unal.edu.co)2


1Universidad EAFIT
2Universidad Nacional de Colombia

This paper appears in: Revista IEEE América Latina

Publication Date: June 2014
Volume: 12,   Issue: 4 
ISSN: 1548-0992


Abstract:
Objective: The aim of this paper is to analyze the development of new forecasting models based on neural networks. Method: We used the systematic literature review method employing a manual search of papers published on new neural networks models in the time period 2000 to 2010. Results: Only 18 studies meet all the requirements of the inclusion criteria. Of these, only three proposals considered a neural networks model using a process different to the autoregressive. Conclusion: Although studies relating to the application of neural network models were frequently present, we find that the studies proposing new forecasting models based on neural networks with a theoretical support and a systematic procedure for the construction of model, were scarce in the time period 2000-2010.

Index Terms:
Forecasting, artificial neural networks, nonlinear time series, ARIMA, ANFIS   


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