SARIMAX
Explore how to use the SARIMAX model to handle seasonal time series components including autoregression, integration, and moving averages. Understand how to include exogenous variables to enhance forecasting accuracy with Python, enabling you to model complex seasonal patterns and external influences effectively.
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Understanding SARIMAX
To review, 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 SARIMAX 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