Time series decomposition

When visualizing time series data, you should look out for some distinguishable patterns:

seasonality: does the data display a clear periodic pattern?
trend: does the data follow a consistent upwards or downward slope?
noise: are there any outlier points or missing values that are not consistent with the rest of the data?

You can rely on a method known as time-series decomposition to automatically extract and quantify the structure of time-series data. The statsmodels library provides the seasonal_decompose() function to perform time series decomposition out of the box.

decomposition = sm.tsa.seasonal_decompose(time_series)

You can extract a specific component, for example seasonality, by accessing the seasonal attribute of the decomposition object.

This exercise is part of the course

Visualizing Time Series Data in Python

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Exercise instructions

Import statsmodels.api using the alias sm.
Perform time series decomposition on the co2_levels DataFrame into a variable called decomposition.
Print the seasonality component of your time series decomposition.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Import statsmodels.api as sm
import ____ as ____

# Perform time series decompositon
decomposition = sm.tsa.____(____)

# Print the seasonality component
print(____)

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