The idea
With bottleneck move_mean, I can compute more effectively the result of the pandas rolling().mean() chain. It could be good to have an equivalent for rolling().apply(fct,kwargs).
Expected behavior
Then, we will have the possibility to compute any function with a moving window and particularly statistical metrics (mean square error, median square error, mean absolute error...).
Additional context
I would be particularly interested in computing a moving mean square error:
Arguments:
data = array of size N
ref = array of size M <N (given as argument)
window of size M
moving function to compute: mean((data[window]-ref)**2)
I don't know how to code in C so I cannot implement the function myself. Can someone do it, please?
The idea With bottleneck move_mean, I can compute more effectively the result of the pandas rolling().mean() chain. It could be good to have an equivalent for rolling().apply(fct,kwargs).
Expected behavior Then, we will have the possibility to compute any function with a moving window and particularly statistical metrics (mean square error, median square error, mean absolute error...).
Additional context I would be particularly interested in computing a moving mean square error: Arguments:
I don't know how to code in C so I cannot implement the function myself. Can someone do it, please?