Research Article Open Access

Correlation Based ADALINE Neural Network for Commodity Trading

J. Chandra1, M. Nachamai1 and Anitha S. Pillai2
  • 1 Christ University, India
  • 2 Hindustan University, India
Journal of Computer Science
Volume 11 No. 7, 2015, 863-871

DOI: https://doi.org/10.3844/jcssp.2015.863.871

Submitted On: 10 September 2015
Published On: 29 October 2015

How to Cite: Chandra, J., Nachamai, M. & Pillai, A. S. (2015). Correlation Based ADALINE Neural Network for Commodity Trading. Journal of Computer Science, 11(7), 863-871. https://doi.org/10.3844/jcssp.2015.863.871

Abstract

Commodity trading is one of the most popular resources owning to its eminent predictable return on investment to earn money through trading. The trading includes all kinds of commodities like agricultural goods such as wheat, coffee, cocoa etc. and hard products like gold, rubber, crude oils etc.,. The investment decision can be made very easily with the help of the proposed model. The proposed model correlation based multi layer perceptron feed forward adaline neural network is an integrated method to forecast the future values of all commodity trading. The correlation based adaline neuron is used as an optimized predictor in the multi layer perceptron feed forward neural network. The correlation is used for feature selection before building the predictive model. The aim of the paper is to build the predictive model for commodity trading. The model is created using correlation based feature selection and adaline neural network to prognosticate all future values of commodities. The adaptive linear neuron is formed with the help of linear regression. To implement the proposed model the live data is captured from mcxindia. The mcxindia is considered as one the popular website for doing commodities and derivatives in India. To train the proposed model, few random samples are used and the model is evaluated with the help of few test samples from the same data set.

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Keywords

  • Return on Investments (ROI)
  • Artificial Neural Network (ANN)
  • Multilayer Perceptron (MLP)
  • Adaptive Linear Neuron (ADALINE)
  • Correlation Based Feature Selection (CBFS)
  • Mean of Magnitude Relative Error (MMRE)