R help - Jul 2010 - please give us some suggestions regarding a modified PCA algo

Hi all,

Let's say we have M x N matrix, which represents N time series, each having
M observations in order of time.

How do we find maximal number of linear combinations of these N time series,
their mutual correlation has to be less than certain pre-specified
constraints.

That's to say, we would like to find as many combinations of the N time
series as possible, such that their mutual correlation remains below a
bound.

Our understanding is that with the help from PCA, we will be able to find
probably N such combinations, expressed in the form of eigenvectors, such
that the N resultant newly constructed time series have 0 correlation
(orthogonal).

But we now want to relax the problem from 0 correlation to within a certain
bound.

Your thoughts and pointers are highly appreciated. Thank you!

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