Predictive Analytics: Microsoft Excel, 2nd Edition

11 Box-Jenkins ARIMA Models

IN THIS CHAPTER

The Rationale for ARIMA

Stages in ARIMA Analysis

The Identification Stage

The Estimation Stage

The Diagnostic and Forecasting Stages

Suppose you’re fortunate enough to have at hand a nice long baseline of observations. You’d like to model how the data behaves over time, and if possible create a credible forecast. You have your choice of approaches for that task. As Chapter 4, “Forecasting a Time Series: Smoothing,” and Chapter 6, “Forecasting a Time Series: Regression,” showed, you could take a smoothing approach—that is, a variation on moving averages—or a regression approach, possibly one based on autoregression so that you’re using the baseline to forecast itself.

How do you choose which approach, ...