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A local linear radial basis function neural network for financial time-series forecasting

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Abstract

In this paper a Local Linear Radial Basis Function Neural Network (LLRBFN) is presented. The difference between the proposed neural network and the conventional Radial Basis Function Neural Network (RBFN) is connection weights between the hidden layer and the output layer which are replaced by a local linear model in the LLRBFN. A modified Particle Swarm Optimization (PSO) with hunter particles is introduced for training the LLRBFN. The proposed methods have been applied for prediction of financial time-series and the result shows the feasibility and effectiveness.

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Authors and Affiliations

  1. Biomedical Engineering Group, Electrical Engineering Department, Iran University of Science and Technology, Tehran, Iran

    Vahab Nekoukar

  2. Control Group, Electrical Engineering Department, Tarbiat Modares University, Tehran, Iran

    Mohammad Taghi Hamidi Beheshti

Authors
  1. Vahab Nekoukar
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  2. Mohammad Taghi Hamidi Beheshti
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Correspondence to Vahab Nekoukar.

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Nekoukar, V., Hamidi Beheshti, M.T. A local linear radial basis function neural network for financial time-series forecasting. Appl Intell 33, 352–356 (2010). https://doi.org/10.1007/s10489-009-0171-1

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  • DOI: https://doi.org/10.1007/s10489-009-0171-1

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