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RETRACTED: An Evolutionary Technique for Building Neural Network Models for Predicting Metal Prices

Author

Listed:
  • Devendra Joshi

    (Department of CSE, Koneru Lakshmaiah Education Foundation, Guntur 522302, Andhra Pradesh, India)

  • Premkumar Chithaluru

    (Department of Computer Science and Engineering, Chaitanya Bharathi Institute of Technology, Hyderabad 500075, Telangana, India
    Department of Project Management, Universidad Internacional Iberoamericana, Campeche 24560, Mexico
    Uttaranchal Institute of Technology, Uttaranchal University, Dehradun 248007, Uttarakhand, India)

  • Divya Anand

    (Department of Project Management, Universidad Internacional Iberoamericana, Campeche 24560, Mexico
    Department of Computer Science and Engineering, Lovely Professional University, Phagwara 144411, Punjab, India
    Higher Polytechnic School, Universidad Europea del Atlántico, C/Isabel Torres 21, 39011 Santander, Spain)

  • Fahima Hajjej

    (Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia)

  • Kapil Aggarwal

    (Department of CSE, Koneru Lakshmaiah Education Foundation, Guntur 522302, Andhra Pradesh, India)

  • Vanessa Yelamos Torres

    (Higher Polytechnic School, Universidad Europea del Atlántico, C/Isabel Torres 21, 39011 Santander, Spain
    Department of Project Management, Universidad Internacional Iberoamericana, Arecibo, PR 00613, USA
    Engineering Research and Innovation Group, Universidade Internacional do Cuanza, Estrada Nacional 250, Bairro Kaluapanda, Cuito EN250, Angola)

  • Ernesto Bautista Thompson

    (Department of Project Management, Universidad Internacional Iberoamericana, Campeche 24560, Mexico
    Higher Polytechnic School, Universidad Europea del Atlántico, C/Isabel Torres 21, 39011 Santander, Spain
    Department of Project Management, Universidad Internacional Iberoamericana, Arecibo, PR 00613, USA)

Abstract

In this research, a neural network (NN) model for metal price forecasting based on an evolutionary approach is proposed. Both the neural network model’s network parameters and network architecture are selected automatically. The time series metal price data set is used to construct a novel fitness function that takes into account both error minimizations and the reproduction of the auto-correlation function. Calculating the average entropy values allowed the selection of the input parameter count for the neural network model. Gold price forecasting was performed using the proposed methodology. The optimal hidden node number, learning rate, and momentum are 9, 0.026, and 0.76, respectively, according to the evolutionary-based NN model. The proposed strategy is shown to reduce estimation error while also reproducing the auto-correlation function of the time series data set by the validation results with gold price data. The performance of the proposed method is better than other current methods, according to a comparison study.

Suggested Citation

  • Devendra Joshi & Premkumar Chithaluru & Divya Anand & Fahima Hajjej & Kapil Aggarwal & Vanessa Yelamos Torres & Ernesto Bautista Thompson, 2023. "RETRACTED: An Evolutionary Technique for Building Neural Network Models for Predicting Metal Prices," Mathematics, MDPI, vol. 11(7), pages 1-19, March.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:7:p:1675-:d:1112755
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    References listed on IDEAS

    as
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