Neural learning: price prediction for non-agricultural commodities using back propagation network

  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract

    Neural Network is relatively superlative in predicting economic data. The concept for econometric research contracts with predicting the price variation of non-agricultural commodities. With a focus on gold, silver, aluminium, lead, zinc, natural gas, crude oil the systematic learning for finding the price growth is the aim of this research. The methodology implemented deals with neural network back propagation for training and testing. The input data are learned with 0.2 learning rate and trained until minimization of error. The price of describing commodities from 2007 to 2016 year and it is predicted that within this period the importing commodities get profit up to 77% with combination of gold, silver, aluminium and crude oil are sold as equally. Systematically, the goods are learned easily in the proposed methodology and the error rate is minimized at the lowest.


  • Keywords

    Neural Network; Back Propagation; Non-Agricultural Commodities; Price; Profit Estimation.

  • References

      [1] S. Kumar Chandar, M. Sumathi, S.N. Sivanadam,” Forecasting Gold Prices Based On Extreme Learning Machine”, International Journal Of Computers Communications & Control ISSN 1841-9836, 11(3):372-380, June 2016.

      [2] Ching-Hwang Wang, Chih-Han Kao, Wei-Hsien Lee,” A new interactive model for improving the learning performance of back propagation neural network”, Automation in Construction 16 (2007) 745–758.

      [3] Massimo Panella, Francesco Barcellona Valentina Santucci and Rita L. D’Ecclesia ,” Neural Networks to Model Energy Commodity Price Dynamics”.

      [4] V. Mary Kiruba Rani, S.S. Dhenakran,” A Mathematical Modelling for Quality Based Ultrasound Breast Cancer Image Using ColourProperties”, Australian Journal of Basic and Applied Sciences, 10(2) Special 2016, Pages: 44-51.

      [5] Girish K. Jha and Kanchan Sinha,” Agricultural Price Forecasting Using Neural Network Model: An Innovative Information Delivery System”, Agricultural Economics Research Review Vol. 26 (No.2) July-December 2013 pp 229-239.

      [6] G. Hudson Arul Vethamanikam, S. Rajamohan,” Unit Root Test Of Selected Non-Agricultural Commodities And Macro Economic Factors In Multi Commodity Exchange Of India Limited”, International Journal of Advanced Research in Management and Social Sciences, ISSN: 2278-6236,Impact Factor: 6.943.

      [7] Pushpa Mohan, Kiran KumariPatil, “Crop Cost Forecasting using Artificial Neural Network with feed forward back propagation method for Mysore Region”, International Journal of Innovative Research in Science, Engineering and Technology, Vol. 6, Issue 4, April 2017.




Article ID: 12616
DOI: 10.14419/ijet.v7i4.12616

Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.