Solved – How to generate stationary and non-stationary time series data in python

I need custom simulated stationary and non-stationary(trending) time series data for one of my python projects. I searched all the web for it but didn't find anything for python. Then I came across this post Is a model with a sine wave time-series stationary? where a stationary series is explained as
yt=yt−1+ϵ where μ(ϵ)=0 in the comment section. I coded a sample code for generating stationary series

import matplotlib.pyplot as plt import numpy as np  n=100 x=np.arange(n) y_=np.random.uniform(-1,1,[n])  mu=0 sigma=0.01 e= np.random.normal(mu, sigma, n) #stationary series y=y_+e  plt.plot(x,y) 

the output looks like a stationary time series but I am not sure of it.

But the most difficult part is finding a way to generate non-stationary(ie. trending) time series data. I can't find anything releated to it. How can we generate stationary and non-stationary time series data in python?

You need to come up with a data generating process (DGP), because simply saying "stationary" is too broad, there's too many processes that fall into this bucket. For instance, this is stationary $y_t=varepsilon_t$, where $varepsilon_tsimmathcal N(0,1)$ as well as this too $x_t=varepsilon_t/2+varepsilon_{t-1}/4$ etc.

The first one is easy to generate in any language, it's just a bunch of independent random numbers: y= np.random.normal(0, 1, n) Obviously, you can change the mean and the variance, or pick any other distribution, e.g. Poisson

The nonstationary is also a very broad category: anything with changing mean or variance. You can change mean and variance deterministically or stochastically. The simplest process could be: $y_t=y_{t-1}varepsilon_t$ or even $y_t=y_{t-1}+varepsilon_t$, which you can generate by simple recursion or a loop

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