Time Series Forecasting in Python

9 Adding external variables to our model

This chapter covers

• Examining the SARIMAX model
• Exploring the use of external variables for forecasting
• Forecasting using the SARIMAX model

In chapters 4 through 8, we have increasingly built a general model that allows us to consider more complex patterns in time series. We started our journey with the autoregressive and moving average processes before combining them into the ARMA model. Then we added a layer of complexity to model non-stationary time series, leading us to the ARIMA model. Finally, in chapter 8 we added yet another layer to ARIMA that allows us to consider seasonal patterns in our forecasts, which resulted in the SARIMA model.

So far, each model that we have explored and used to produce ...