Forecasting seasonal data using ARIMA

        
Time series often display periodic behavior so that peaks or dips in the value appear at regular intervals. This behavior is called seasonality in the analysis of time series. The methods we have used to far in this chapter to model time series data obviously do not account for seasonality. Fortunately, it is relatively easy to adapt the standard ARIMA model to incorporate seasonality, resulting in what is sometimes called a SARIMA model.
        
In this recipe, we will learn how to model time series data that includes seasonal behavior and use this model to produce forecasts.
        
Getting ready
        
For this recipe, we will need the NumPy package imported as np, the Pandas package imported as pd, the Matplotlib pyplotmodule as plt, and the statsmodels apimodule imported as sm. We will also need the utility for creating sample time series data from the tsdatamodule, which is included in this book's repository:
        
from tsdata...