We have covered a lot of statistical models for time series forecasting. Back in chapters 4 and 5, you learned how to model moving average processes and autoregressive processes. We then combined these models to form the ARMA model and added a parameter to forecast non-stationary time series, leading us to the ARIMA model. We then added a seasonal component with the SARIMA model. Adding the effect of exogenous variables culminated in the SARIMAX model. Finally, we covered multivariate time series forecasting using the VAR model. Thus, you now have access to many statistical models that allow you to forecast a wide variety of time series, from simple to more complex. This is a good time to consolidate your learning and put your knowledge into practice with a capstone project.