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A Review of Artificial Neural Networks: How Well Do They Perform in Forecasting Time Series?

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  • Elsy Gómez-Ramos

    (Escuela Superior de Economía, Instituto Politécnico Nacional, D. F. México, México)

  • Francisco Venegas-Martínez

    (Escuela Superior de Economía, Instituto Politécnico Nacional, D. F. México, México)

Abstract

At the beginning of the 90’s, Artificial Neural Networks (ANNs) started their applications in finance. The ANNs are data-drive, self-adaptive and non-linear methods that do not require specific assumptions about the underlying model. In general, there are five groups of networks used as forecasting tools: 1) Feedforward Networks, like the Multilayer Perceptron (MLP), 2) Recurrent Networks, 3) Polynomial Networks, 4) Modular Networks, and 5) Support Vector Machine. This paper carries out a review of the specialized literature on ANNs and makes a comparative analysis according to their performance in forecasting stock indices and exchange rates. The objective is to assess the performance when applying different types of networks in relation to MLP. It is shown that the MLP is the best network in forecasting time series. However, it is shown that the MLP has important delimitations in several respects: network architecture, basic functions and initialization weights.

Suggested Citation

  • Elsy Gómez-Ramos & Francisco Venegas-Martínez, 2013. "A Review of Artificial Neural Networks: How Well Do They Perform in Forecasting Time Series?," Analítika, Analítika - Revista de Análisis Estadístico/Journal of Statistical Analysis, vol. 6(2), pages 7-15, Diciembre.
    • Handle: RePEc:inp:inpana:v:6:y:2013:i:2:p:7-15
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    References listed on IDEAS

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    1. Andrea Manni & Giovanna Saviano & Maria Grazia Bonelli, 2021. "Optimization of the ANNs Predictive Capability Using the Taguchi Approach: A Case Study," Mathematics, MDPI, vol. 9(7), pages 1-16, April.

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    More about this item

    Keywords

    Artificial neural networks; Multilayer Perceptron; Forecasting time series;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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