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Performance analysis of NARX neural network backpropagation algorithm by various training functions for time series data

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  • D. Ashok Kumar
  • S. Murugan

Abstract

This study seeks to investigate the various training functions with non-linear auto regressive eXogenous neural network (NARXNN) to forecasting the closing index of the stock market. An iterative approach strives to adjust the number of hidden neurons of a NARXNN model. This approach systematically constructs different NARXNN models from simple architecture to complex architecture with different training functions and finds the optimum NARXNN model. The effectiveness of the proposed approach was seen to be a step ahead of Bombay Stock Exchange (BSE100) closing stock index of the Indian stock market. This approach has identified optimum neuron counts in the hidden layer for every training function with NARXNN, which reduces neural network (NN) structure and training time and increases the convergence speed. The experimental result reveals that neuron counts in the hidden layer cannot be identified by some rule of thumb.

Suggested Citation

  • D. Ashok Kumar & S. Murugan, 2018. "Performance analysis of NARX neural network backpropagation algorithm by various training functions for time series data," International Journal of Data Science, Inderscience Enterprises Ltd, vol. 3(4), pages 308-325.
    • Handle: RePEc:ids:ijdsci:v:3:y:2018:i:4:p:308-325
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    Cited by:

    1. Karodine Chreng & Han Soo Lee & Soklin Tuy, 2022. "A Hybrid Model for Electricity Demand Forecast Using Improved Ensemble Empirical Mode Decomposition and Recurrent Neural Networks with ERA5 Climate Variables," Energies, MDPI, vol. 15(19), pages 1-26, October.

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