# How are the values of the ARMA process linked in python

In the code below, you can see that 'ret' is an ARMA process, and I am trying to see how the ret[0], etc... ret3, ret4, etc. are linked to each other, and although I know the formula for the ARMA process (see it below) , I cannot manually reproduce e.g ret[4] from the previous values... Any help?

I tried to reproduce it as below, but it does not give ret[4]

Below you can find the code in text:

import pandas as pd
import numpy as np

np.random.seed(200)

ar_params = np.array([1, -0.5])
ma_params = np.array([1, -0.3])

print(ar_params)
print(ma_params)

from statsmodels.tsa.arima_process import ArmaProcess

length = 5 * 252
np.random.seed(200)
ret = ArmaProcess(ar_params, ma_params).generate_sample(nsample=length)

ret = pd.Series(ret)
print('========================')
print("\nret = ", ret) ;


First, note that $$\epsilon_t \sim N(0,1)$$ is a white noise process and the random variates are simulated from a standard normal distribution. Hence, it does not make sense for you to multiply ret[2] and ret[3] with the MA-parameters, in order to reproduce ret[4].

### The source code reveals how to reproduce the simulation values of the ARMA process:

From the source code of the ArmaProcess().generate_sample() function (see here), you will find that it effectively uses the filter algorithm from the scipy package called signal.lfilter(). The signal filter from the scipy package, can be defined as (again, see this link):

$$$$\phi_0 \cdot y_t = \theta_0 \epsilon_t + \theta_{1} \epsilon_{t-1} + \ldots+\theta_{q} \epsilon_{t-q} - \phi_1 y_{t-1} - \ldots - \phi_p y_{t-p} ,$$$$ where $$p$$ and $$q$$ refers to the number of lagged terms in the filter algorithm. One way of transforming the above filter algorithm to an ARMA(p,q) process is simply to set $$\phi_0 = \theta_0 = 1$$ and negate the rest of the phi's. Both of these things are also noted in the documentation of the ArmaProcess().generate_sample() function, which is the reason why your above code contains 1's in the first entry of the ar_params and ma_params. Now, we can observe that the above code simulates an ARMA(1,1) process and assuming that you have already negated the AR-parameter, $$\phi_1$$, then ret[4] ($$y_4$$) can be found via:
$$$$y_4= \epsilon_4 + (- 0.3) \cdot \epsilon_{3} - y_{3} \cdot (-0.5) = 0.5 \cdot y_3 - 0.3 \cdot \epsilon_3 + \epsilon_4.$$$$ I have provided some Python code that recovers the ArmaProcess().generate_sample() simulation using the signal.lfilter() method, which can be seen below:

import pandas as pd
import numpy as np
import scipy as sc

np.random.seed(200)
whitenoise = np.random.normal(size=length)
test = sc.signal.lfilter(ma_params, ar_params, whitenoise)
test = pd.Series(test)
test


From your own code (or you could've used test) and the above described equation, we can manually calculate ret[4]:

ret4 =  ma_params[0] * whitenoise[4]  + ma_params[1] * whitenoise[3] - ret[3] * ar_params[1]
ret4


I encourage you to read the documentation of the signal.lfilter() function to further understand how the algorithm initializes etc. However, this should be the gist of it. I hope this provide some guidance.