Seasonality
Explore the concept of seasonality in time series data and learn how to apply the SARIMAX model in Python to capture seasonal patterns. Understand the roles of autoregressive, integration, and moving average components within seasonal order settings to improve forecasting accuracy.
Using seasonality for forecasting
Seasonality refers to periodical patterns in the data, and it's a critical component of forecasting, particularly for data in which seasonal variations are big and consistent. Even though we can identify it visually, to use it for forecasting, we need a more complex model such as SARIMAX.
SARIMAX combines multiple time series components—Seasonality, Autoregression, Integration, Moving Average, and Exogenous variables. It takes essentially two parameters—order, which refers to autoregression, integration, and moving average, and seasonal_order, which refers to seasonality. To use it for seasonality purposes only, all we need to do is set order = (0,0,0) and work only with seasonal_order.
Let's understand how seasonal_order(P, D, Q, M) works by calling the function parameters