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

Chapter 6
Time Series
ì
ï
ï
r ( k)=
í
ï
ï
ïî
q-k
å
bb
i i+
k
i=
0 i=
0
1 k = 0
q
å
2
/ b k= 1, ¼,
q
i
0 k>
q
r (-k) k<
0
Fitting Moving Average Process
The moving-average (MA) model is a well-known approach for realizing a
single-variable weekly stationary time series (see Figure 6-3). The movingaverage
model specifies that the output variable is linearly dependant on
its own previous error terms as well as on a stochastic term. The AR model
is called the Moving-Average model, which is a special case and a key
component of the ARMA and ARIMA models of time series.
X = e + j X + qe + h d
t
t
p
å å å
q
i t-i
i t-i
i
i=
1 i=
1 i=
1
b
t-i
Figure 6-3. Example of moving average
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