A moving window average is a poor digital filter in the frequency domain [0]. You can get better results with more control on which frequencies are removed by implementing a FIR or IIR (especially Butterworth) digital filter.

[0] https://ptolemy.eecs.berkeley.edu/eecs20/week12/freqResponse...

Yes, optimizes window size for moving average

Optimal detail is measure by:

* minimizing variance of 1-step difference (point-to-point fluctuation)

* maintaining kurtosis (outliers not lost)

+optimizations to reduce search space

Comments are saying yes- but is it once you find the optimal window size, you fix it? Or does the window size vary over the data? Because if the latte wouldn't that be disingenuous to outliers and such?

Yes.