Handling seasonality – seasonal differencing
In this recipe, we show how differencing can be used to model seasonal patterns in time series.
Getting ready
We’ve learned to use first differences to remove the trend from time series. Differencing can also work for seasonality. But, instead of taking the difference between consecutive observations, for each point, you subtract the value of the previous observation from the same season. For example, suppose you’re modeling monthly data. You perform seasonal differencing by subtracting the value of February of the previous year from the value of February of the current year.
The process is similar to what we did with first differences to remove the trend. Let’s start by loading the data:
time_series = df["Incoming Solar"] train, test = train_test_split(time_series, test_size=0.2, shuffle=False)
In this recipe, we’ll use seasonal differencing to remove yearly seasonality.
