How to do it...

In the following steps, we demonstrate several methods for making predictions using time series data:

1. Begin by generating a time series:

from random import random

time_series = [2 * x + random() for x in range(1, 100)]

1. Plot your data:

%matplotlib inline
import matplotlib.pyplot as plt

plt.plot(time_series)
plt.show()

The following screenshot shows the output:

1. There is a large variety of techniques we can use to predict the consequent value of a time series:
   • Autoregression (AR):

from statsmodels.tsa.ar_model import AR

model = AR(time_series)
model_fit = model.fit()
y = model_fit.predict(len(time_series), len(time_series))

1. • Moving average (MA):

from statsmodels.tsa.arima_model import ARMA

model = ARMA(time_series, order=(0, 1))
model_fit = model.fit(disp=False)
y = model_fit.predict(len(time_series), len(time_series))

1. • Simple exponential smoothing (SES):

from statsmodels.tsa.holtwinters import SimpleExpSmoothing

model = SimpleExpSmoothing(time_series)
model_fit = model.fit()
y = model_fit.predict(len(time_series), len(time_series))

The resulting predictions are as follows: