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Apliying PCA to two correlated stochastic processes
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[QUOTE="Frank Einstein, post: 6015996, member: 517219"] Hello everyone, I have two matrices of size 9*51, meaning that I have 51 measurements of a stochastic process measured at 9 times, being precise, it is wind speed in the direction X, I have the same data for the direction Y. I am aware that both stochastic processes are not independent, so I would like to use PCA over both of them at the same time. My software of choice is Matlab. I cam perform the PCA analysis simply as: [coeffU, scoreU, latentU, tsquaredU, explainedU, muU]=pca(U,'Centered',false), however, if I try to execute it over UV, being UV equal to cat(3,U,V), it doesn't work. Can anyone tell me if there is a way of finding the joint main directions of variation instead of having to compute each one apart? Thanks for Reading. [/QUOTE]
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Apliying PCA to two correlated stochastic processes
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