[- forecasting methods]
b-4) PRINCIPAL COMPONENT ANALYSISwikipedia.com - "principal component analysis (PCA) involves a mathematical procedure that transforms a number of possibly correlated variables into a smaller number of uncorrelated variables called principal components"
its basic structure, equivalently to the one from PLS regressions, consists in two matrices, X [independent variables] & Y [dependent variable(s)] - the differences between both methods are a consequence from the mathematical models used to relate these matrices
linear model -> PLS regression
hyperplanes of minimum variance -> principal component analysis
the aforementioned distinction is not relevant for the present study and, thus the PLS regression post describable enough
linear model -> PLS regression
hyperplanes of minimum variance -> principal component analysis
the aforementioned distinction is not relevant for the present study and, thus the PLS regression post describable enough
