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Prediction of Financial Time Series Data using Hybrid Evolutionary Legendre Neural Network: Evolutionary LENN

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  • Rajashree Dash

    (Siksha ‘O' Anusandhan University, Bhubaneswar, India)

  • Pradipta Kishore Dash

    (Siksha ‘O' Anusandhan University, Bhubaneswar, India)

Abstract

In this paper a predictor model using Legendre Neural Network is proposed for one day ahead prediction of financial time series data. The Legendre Neural Network (LENN) is a single layer structure that possess faster convergence rate and reduced computational complexity by increasing the dimensionality of the input pattern with a set of linearly independent nonlinear functions. The parameters of the LENN model are estimated using a Moderate Random Search Particle Swarm Optimization Method (HMRPSO). The HMRPSO is a variant of PSO that uses a moderate random search method to enhance the global search ability of particles and increases their convergence rates by focusing on valuable search space regions. Training LENN using HMRPSO has also been compared with Particle Swarm Optimization (PSO) and Differential Evolution (DE) based learning of LENN for predicting the Bombay Stock Exchange and S&P 500 data sets.

Suggested Citation

  • Rajashree Dash & Pradipta Kishore Dash, 2016. "Prediction of Financial Time Series Data using Hybrid Evolutionary Legendre Neural Network: Evolutionary LENN," International Journal of Applied Evolutionary Computation (IJAEC), IGI Global, vol. 7(1), pages 16-32, January.
    • Handle: RePEc:igg:jaec00:v:7:y:2016:i:1:p:16-32
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    Cited by:

    1. Dash, Rajashree, 2017. "An improved shuffled frog leaping algorithm based evolutionary framework for currency exchange rate prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 782-796.

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