Rolling window features for multiclass classification I'm doing a multiclass classification and data is considered as not being a time-series. Working on a feature engineering and trying to solve the problem with classic KNN, RF, boosting etc. I'm creating new features based on rolling window and found out that people usually do mean aggregation.

Does the mean characteristic of rolling distribution is the most informative?
Are there any other characteristics (like std, quantilies, IQR, etc.) that may be used?
Is it worth to perform some kind of data transformation (like scaling, quantile transfomation or Box-Cox)?
Usually the point of a rolling window is to calculate and use a moving average:

A moving average is commonly used with time series data to smooth out short-term fluctuations and highlight longer-term trends or cycles.

By definition the only meaningful aggregation for this kind of purpose is the mean: the idea is to represent the same type of value as the original series while correcting noisy variations.
Anything else is not the standard use case for a rolling window, so the answer to questions 2 and 3 would completely depend on the application.