20 Capstone: Forecasting the monthly average retail price of steak in Canada

 

This chapter covers

  • Developing a forecasting model to predict the monthly average retail price of steak in Canada
  • Using Prophet’s cross-validation functionality
  • Developing a SARIMA model and comparing its performance to Prophet to determine the champion model

Again, congratulations on making it this far! We have come a long way since the beginning of this book. We first defined time series and learned how to forecast them using statistical models that generalize as the SARIMAX model. Then we turned to large, high-dimensional datasets and used deep learning for time series forecasting. In the previous chapter, we covered one of the most popular libraries for automating the entire forecasting process: Prophet. We developed two forecasting models using Prophet and saw how quick and easy it is to generate accurate predictions with few manual steps.

In this last capstone project, we’ll use everything you have learned in this book to forecast the monthly average retail price of steak in Canada. At this point, we have a robust methodology and a wide array of tools to develop a performant forecasting model.

20.1 Understanding the capstone project

20.1.1 Objective of the capstone project

20.2 Data preprocessing and visualization

20.3 Modeling with Prophet

20.4 Optional: Develop a SARIMA model

20.5 Next steps