Performing time series decomposition using the stl() function

Nearly every phenomenon can be represented as a time series.

It is therefore not surprising that time series analysis is one of most popular topics within data-science communities.

As is often the case, R provides a great tool for time-series decomposition, starting with the stl() function provided within base R itself. This function will be the base of our recipe.

Getting ready

This recipe will mainly use the stl() function, which implements the Loess() method for time-series decomposition.

Using this method, we are able to separate a time series into three different parts:

• Trend component: This highlights the core trend of the phenomenon if perturbations and external influence were not in place

• Seasonal component: This is linked to cyclical influences

• Remainder: This groups all non-modeled (in hypothesis random) effects

As mentioned earlier, this function is provided with every R base version, and we therefore don't need to install...