Chapter 10 Summary

Chapter 10 focuses on time series forecasting techniques applied to predicting quarterly sales for Apple Inc. The chapter uses Apple's sales data from 2006 to 2022 as a case study to demonstrate various forecasting models.

The chapter begins by outlining learning objectives, which include understanding the concept of time series forecasting, preparing datasets with explanatory variables based on time series (such as time trends and lagged values), estimating models using these variables, validating model accuracy, and forecasting out-of-sample values. It then introduces the business context, explaining the importance of revenue forecasting for investors and companies.

The document provides step-by-step instructions on using Alteryx software to prepare the data, create new variables (such as lagged sales and iPhone release dummy variables), and build various forecasting models. It covers three main models: a simple linear trend model, a lagged sales model, and a model combining trend with a dummy variable for iPhone releases. The chapter includes several in-chapter practice problems and emphasizes the importance of creating and interpreting prediction intervals for audit applications. It concludes with takeaway points highlighting the importance of combining knowledge from time series analysis and dummy variables to improve predictive accuracy.