چکیده (انگلیسی):

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.