Summary

This chapter started with an introduction to time series. We provided an overview of what a time series is and how it can be used to meet specific goals. We also discussed the criteria for differentiating time-series data from data that does not depend on time. We also discussed stationarity, which factors are important for stationarity, how to measure them, and how to resolve cases where stationarity does not exist. From there, we were able to understand the primary functions of ACF and PACF analysis and for making inferences about processes using variance around the mean. Additionally, we provided an introduction to time-series modeling with an overview of the white-noise model and the basic concepts behind autoregressive and moving average components, which help form the basis of ARIMA and seasonal autoregressive integrated moving average (SARIMA) time-series models.

In Chapter 11, ARIMA Models, we will also move deeper into the discussion of autoregressive, moving average...