As described in Advances of Machine Learning by Marcos Prado.
This example reproduces the visual seen in the book of Marcos Prado.
Via PyPI.
pip install fracdiff2
As straightforward as possible.
from fracdiff2 import frac_diff_ffd
import numpy as np
x = np.random.uniform(size=(1000,))
frac_diff_ffd(x, d=0.5)- https://www.wiley.com/en-us/Advances+in+Financial+Machine+Learning-p-9781119482086
- https://wwwf.imperial.ac.uk/~ejm/M3S8/Problems/hosking81.pdf
- https://en.wikipedia.org/wiki/Fractional_calculus
This repository was written in 2018. It seems like a new faster library was created in 2022: fracdiff. It is worth checking it out.