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I have a timeseries dataset with large number of features. Because of that, I need to do PCA. Some of the features are noisy, others are not.

My question is - does it make to do some kind of smoothing (filtering) such as moving average, before doing PCA?

It seems that this might additionally reduce number of eigenvectors needed to represent the original dataset, as the variance will be smaller after smoothing.

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  • $\begingroup$ Some discussion here: stats.stackexchange.com/questions/23566 $\endgroup$
    – amoeba
    Commented Dec 14, 2016 at 14:59
  • $\begingroup$ Wow, thanks for the super-useful, and very quick link. I was actually expecting that this question will rust here. So, basically, my feeling is good? It does make sense to do the smoothing (such as rolling avg) before PCA? $\endgroup$
    – Kobe-Wan Kenobi
    Commented Dec 14, 2016 at 15:36

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