Advanced Data Analytics Using Python_ With Machine Learning, Deep Learning and NLP Examples ( 2023)

Chapter 6
Time Series
Here’s the example code for a moving average model:
import numpy as np
def running_mean(l, N):
# Also works for the(strictly invalid) cases when N is even.
if (N//2)*2 == N:
N = N - 1
front = np.zeros(N//2)
back = np.zeros(N//2)
for i in range(1, (N//2)*2, 2):
front[i//2] = np.convolve(l[:i], np.ones((i,))/i, mode
= 'valid')
for i in range(1, (N//2)*2, 2):
back[i//2] = np.convolve(l[-i:], np.ones((i,))/i, mode
= 'valid')
return np.concatenate([front, np.convolve(l,
np.ones((N,))/N, mode = 'valid'), back[::-1]])
print running_mean(2,21)
Autoregressive Processes
Suppose {Z t } is a purely random process with mean 0 and variance σ z2 .
After that, a process {X t } is said to be of autoregressive process of order p if
you have this:
x = a x +¼ a x + z , or
t 1 t-1
p t-p t
x = a x + z
t
p
å
i=
1
i i-1
t
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