I have given a time-series in various channels. There are two major oscillations "hidden" in the time-series and distributed over all channels. I want to extract these oscillations using multivariate Singular Spectrum (mSSA) Analysis.
I am new to SSA and it seems to me that SSA is not really a dimensionality reduction method but more a "denoising" method. I.e. is it true that I cannot really extract the major oscillations, as after grouping, backprojection and diagonal averaging, I get signal in all channels, but not really a single signal which is the major oscillation (as PCA would provide)?
On the other hand, the eigenvectors (altough shrinked in time due to hankelization) seem to be exactly the oscillations that I am looking for. Can I use SSA for dimensionality reduction by simply treating the eigenvectors as the major oscillations?
I found an article dealing with exactly the problem i was facing: https://arxiv.org/pdf/1812.09057.pdf
It introduces a technique called "Singular Spectrum Analysis for advanced reduction of dimensionality" (SSA-FARI).