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

D. Ashok Kumar and S. Murugan

International Journal of Data Science, 2018, vol. 3, issue 4, 308-325

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.

Keywords: NARX neural network; time series data; training functions; stock index; forecasting; performance analysis. (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (1)

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