Correlation Based ADALINE Neural Network for Commodity Trading
- 1 Christ University, India
- 2 Hindustan University, India
Published On: 29 October 2015
Copyright: © 2020 J. Chandra, M. Nachamai and Anitha S. Pillai. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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.
- 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)