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.