Chapter 11 Summary

Chapter 11 focuses on advanced time series forecasting techniques, specifically ARIMA (AutoRegressive Integrated Moving Average) models. The chapter builds on the previous chapter's case study of forecasting Apple's quarterly sales.

The chapter begins by outlining learning objectives, which include understanding ARIMA forecasting concepts, preparing datasets for time series analysis, estimating and interpreting ARIMA and ETS (Exponential Smoothing) models, validating model accuracy, and forecasting out-of-sample values. It then provides a brief background on ARIMA models, explaining the three components: Autoregressive (AR), Integrated (I), and Moving Average (MA).

The document provides step-by-step instructions on using Alteryx software to prepare time series data, create ARIMA and ETS models, and compare their performance. It covers concepts such as interpreting model outputs, adding covariates to ARIMA models, creating forecast confidence intervals, and visualizing results. The chapter includes several in-chapter practice problems and emphasizes the importance of avoiding overfitting by using separate training and validation datasets. It concludes with takeaway points highlighting the importance of combining different tools and techniques to improve predictive accuracy in financial forecasting.