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Lets say I have some time series data which I generated like this:

%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from numpy.random import uniform

# Length of time series
N=400

# Gaussian random numbers as an excitation signal
ex = np.random.randn(N)

# Second order AR Process with coefficients slowly changing in time
a0 = np.array([1.2, -0.4])
A = np.zeros((N,2))
omega, alpha = N/2, 0.1

for n in range(N):
    A[n,0] = a0[0] + alpha * np.cos(2*np.pi*n/N)
    A[n,1] = a0[1] + alpha * np.sin(np.pi*n/N)

S = ex.copy()
for n in range(2,N):
    x = np.array([S[n-1], S[n-2]])
    S[n] = np.dot(x, A[n,:]) + ex[n]


fig,ax = plt.subplots(nrows=2, ncols=2, figsize=(9,4))
plt.tight_layout()
ax[1,0].plot(range(N), A[:,0])
ax[1,0].grid(True)
ax[1,0].set_title("Coefficient a0", color='m')
ax[1,1].plot(range(N), A[:,1], color='m')
ax[1,1].grid(True)
ax[1,1].set_title("Coefficient a1", color='m')
ax[0,0].plot(range(N), ex)
ax[0,0].grid(True)
ax[0,0].set_title("Random Excitation Signal")
ax[0,1].plot(range(N), S, color='m')
ax[0,1].grid(True)
ax[0,1].set_title("Time Varying Autoregressive Process")

How do I implement a Sequential Importance Sampling Particle Filter on this data? I understand all the theory and the maths behind it and even saw some examples of implementation on different problems but none of them work with similar data. For example, https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/blob/master/12-Particle-Filters.ipynb deals with robot movements and landmarks and I just want to apply the algorithm to a simple time series data...Can anyone help me with the implementation? I'm especially confused as to how to do the sampling part...

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