Handling seasonality – seasonal decomposition

This recipe describes yet another approach to modeling seasonality, this time using a time series decomposition approach.

Getting ready

We learned about time series decomposition methods in Chapter 1. Decomposition methods aim at extracting the individual parts that make up a time series.

We can use this approach to deal with seasonality. The idea is to separate the seasonal component from the rest (trend plus residuals). We can use a deep neural network to model the seasonally adjusted series. Then, we use a simple model to forecast the seasonal component.

Again, we’ll start with the daily solar radiation time series. This time, we won’t split training and testing to show how the forecasts are obtained in practice.

How to do it…

We start by decomposing the time series using STL. We learned about this method in Chapter 1:

from statsmodels.tsa.api import STL
series_decomp = STL(series, period...