# Exponential Smoothing: Brown Simple Method in Python Videos

Last Update: April 24, 2022

My online video tutorials are hosted at YouTube channel.

For learning this concept, you can view my online video tutorials: Exponential Smooting: Brown Simple Method in Python (Spyder) and Exponential Smoothing: Brown Simple Method in Python (Jupyter).

Videos Code

1. Packages

``````import pandas as pd
import statsmodels.api as sm
import statsmodels.tsa.holtwinters as ets
import matplotlib.pyplot as plt
``````

2. Data

``````mdata_obj = sm.datasets.get_rdataset(dataname="AirPassengers",
package="datasets",
cache=True)
mdata = mdata_obj.data
mdata = pd.DataFrame(data=mdata["value"]).set_index(
pd.date_range(start="1949", end="1961", freq="M"))
print(mdata_obj.__doc__)
``````

Ranges Delimiting

``````tdata = mdata[:"1958-12-31"]
fdata = mdata["1959-01-01":]
``````

3. Exponential Smoothing

Brown Simple Exponential Smoothing

``````tbrown = ets.ExponentialSmoothing(endog=tdata, trend=None,
damped_trend=False,
seasonal=None,
initialization_method=
"estimated").fit()
fbrown = tbrown.forecast(steps=len(fdata))
fbrown = pd.DataFrame(fbrown).set_index(fdata.index)
``````
``````plt.figure()
plt.plot(tdata, label="tdata")
plt.plot(fbrown, label="fbrown")
plt.plot(fdata, label="fdata", linestyle="--")
plt.legend(loc="upper left")
plt.title("Simple Exponential Smoothing")
plt.ylabel("Air Passengers")
plt.xlabel("Year")
plt.show()
``````
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