11 Capstone: Forecasting the number of antidiabetic drug prescriptions in Australia

 

This chapter covers

  • Developing a forecasting model to predict the number of antidiabetic drug prescriptions in Australia
  • Applying the modeling procedure with a SARIMA model
  • Evaluating our model against a baseline
  • Determining the champion model

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.

11.1 Importing the required libraries and loading the data

11.2 Visualizing the series and its components

11.3 Modeling the data

11.3.1 Performing model selection

11.3.2 Conducting residual analysis

11.4 Forecasting and evaluating the model’s performance

Next steps