<p>I found an article dealing with exactly the problem i was facing:
<a href="https://arxiv.org/pdf/1812.09057.pdf" rel="nofollow noreferrer">https://arxiv.org/pdf/1812.09057.pdf</a></p>

<p>It introduces a technique called "Singular Spectrum Analysis for advanced reduction of dimensionality" (SSA-FARI).</p>
Dimensionality reduction using (multivariat) Singular Spectrum Analysis
<p>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.</p>

<p>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)?</p>

<p>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?</p>