Python-Based Simulation of Non-Gaussian Stationary Random Process with Arbitrary Auto-Correlation Function

Simulation of stationary random processes (time series) is an essential engineering tool for system prototyping, design, and optimization. To create a simulation, a randomly generated time series must have a pre-defined distribution and autocorrelation function (ACF). It is challenging to model non-Gaussian distributions as using a linear filter can alter the target distribution. To address this issue, we wrote a Python package that implements one of the prominent methods in the field of random process simulation. To the best of our knowledge, it is the first publicly available implementation of such a simulator in Python.