I have read that for the multivariate form you need to subtract the mean and divide by the standard deviation. Is this necessary before performing basic SSA on one signal?
Thanks
2026-03-26 16:06:42.1774541202
Should mean be subtracted before conducting singular spectrum analysis (SSA)?
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I am learning the Basic SSA and the SSA forecasting techniques now. So far I have implemented and played a little with the Basic SSA in Scilab.
Discarding the mean and normalizing std. dev. is a general approach to normalization for SSA.
From my experience the Basic SSA does work without normalization, but normalization gives you higher values in the eigenvalues spectrum.
I would prefer to hear someone else's interpretation of this fact because I'm not highly experienced in the theory.
There is a very good explanation why the mean should be subtracted before PCA: https://stats.stackexchange.com/a/22331/43304
As an outline: if there is an offset in your data, the first principal component may be related to this offset and not to the actual maximal variance in the data. Also this may cause the first eigenvalue to "eat" much of the total variance and this way make the rest eigenvalues lower.