An Approach for Demand Forecasting in Steel Industries Using Ensemble Learning

Algorithm 1 Steps for the implementation of principal component analysis (PCA).

	Input: dimensional input data matrix with number of samples , and variance threshold
	Output: reduced dimensional data matrix ,
	Load , and calculate mean for each feature, for subtract the mean from each corresponding dimension, for and
	/ Make each signal uncorrelated to each other /
	Calculate covariance matrix of
	Solve the as , where is the matrix of eigenvector and is the diagonal matrix containing eigenvalues on both sides of the diagonal matrix
	Sort the eigenvector matrix in the descending order to the first eigenvector that have variance and form a projection matrix
	Finally, project on the PCA space,