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 thus 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 pyplot module as plt, and the statsmodels api module imported as sm. We will also need the utility for creating sample time series data from the tsdata module, which is included in this book’s repository:

from tsdata import generate_sample_data...