04_decomposition_solutions.ipynb

Kernel: Python 2

Time Series: Decomposition

Independent Practice

Instructor Note: These are optional and can be assigned as student practice questions outside of class.

1) Import the Airline Passengers data set, preprocess the data, and plot the raw time series.

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import datetime
from dateutil.relativedelta import *
%matplotlib inline

airline = pd.read_csv('./data/airline.csv')
airline.head()

airline.tail()

airline.drop(airline.index[144], inplace=True)

airline.columns= ['index','passengers']

airline['passengers'] = airline.passengers.apply(lambda x: int(x))

airline.set_index(['index'], inplace=True)
airline.index.name=None

airline.head() 

airline.plot()

<matplotlib.axes._subplots.AxesSubplot at 0x1a11a9fe48>

2) Decompose the time series and plot using the .seasonal_decompose() function.

from statsmodels.tsa.seasonal import seasonal_decompose

decomposition = seasonal_decompose(airline, freq = 12)

decomposition.plot()
plt.show()

3) Interpret these plots.

# There is a clear upward trend in airline passengers. There is a strong seasonal component.