labs/Module-3/6.Time_series.ipynb

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# Begin by generating a time series
from random import random
time_series = [2*x+random() for x in range(1,100)]

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print(time_series[98])

198.2870997165355

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print(time_series[88:])

[178.7909818421423, 180.89433539778125, 182.37137770579312, 184.04647458530584, 186.58271389967365, 188.23490268057853, 190.194580200028, 192.3947869317429, 194.96947642465653, 196.70073194342214, 198.2870997165355]

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# plot our data
#%matplotlib inline
import matplotlib.pyplot as plt
plt.plot(time_series)
plt.show()

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# Autoregression (AR):

from statsmodels.tsa.ar_model import AutoReg

model = AutoReg(time_series, lags=1)
model_fit = model.fit()
y= model_fit.predict(len(time_series), len(time_series)+10)
print(y)

[200.26729809 202.24704364 204.22633647 206.20517669 208.1835644
 210.1614997  212.1389827  214.1160135  216.09259221 218.06871892
 220.04439375]

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