I have a matrix called realizationMat, it contains 101 measurements of 90 realizations of a stochastic process.

I use KarhunenLoeveDecomposition over it as

`VA1 = realizationMat[[1]]; VA2 = realizationMat[[2]]; VA3 = realizationMat[[3]]; KLVariablesB = KarhunenLoeveDecomposition[{VA1, VA2, VA3, ...}]; `

Note that I have more elements VA, but I don’t write them for the shake of brevity.

In the documentation is written: **rows of the transformation matrix m are the eigenvectors of the covariance matrix formed from the arrays ai.** Also, **The transformed arrays bi are uncorrelated, are given in order of decreasing variance, and have the same total variance as ai.**

However, if I write `ListPlot[{KLVariablesB[[2, 1]]}, Joined -> True]`

, it doesn’t look like an eigenvector at all, see figure 1. Also, if I instead plot `ListPlot[{KLVariablesB[[1, 1]]}, Joined -> True]`

, it looks way more as an eigenvector, see figure 2, yet these aren’t the eigenvectors of the original covariance matrix.

Can someone please tell me what is wrong with my code?

Best regards.