An Evolutionary Technique for Building Neural Network Models for Predicting Metal Prices
Devendra Joshi,
Premkumar Chithaluru,
Divya Anand,
Fahima Hajjej,
Kapil Aggarwal,
Vanessa Yelamos Torres,
Ernesto Bautista Thompson
Affiliations
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
Divya Anand
Department of Project Management, Universidad Internacional Iberoamericana, Campeche 24560, Mexico
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
Ernesto Bautista Thompson
Department of Project Management, Universidad Internacional Iberoamericana, Campeche 24560, Mexico
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.
