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  3. Visualizing Time Series Data in Python

Exercise

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.

Instructions

100 XP
  • 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.