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Consider we have one dimension time samples. The noise in the samples made some portions of the time samples amplitude very high. Obviously the short variance for those time samples will have high variance. I want to do running variance (of some window size) and normalize data by dividing by the variance. Is this procedure would destroy the phase of the phase of the signal? Is there a way to normalize data with unit variance without distorting the phase?

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  • $\begingroup$ removed the completely unrelated tags: your question isn't a soft one, and this isn't about filters. $\endgroup$ – Marcus Müller Jul 22 at 6:18
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"Normalizing the variance": Since variance is a real number, normalizing it means multiplying the signal with a real number.

That doesn't change the phase, ever.

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  • $\begingroup$ Thanks or the answer, running variance is not single number but a set/series of numbers $\endgroup$ – Creator Jul 22 at 6:26
  • $\begingroup$ oh, true, that. $\endgroup$ – Marcus Müller Jul 22 at 9:28
  • $\begingroup$ Hi: if you normalized by dividing by one variance, things would be fine. but, if you use a running variance, then you'll be dividing the series by different values at different points in time which totally changes the series which of course changes the phase. $\endgroup$ – mark leeds Jul 23 at 5:03

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