## Course Outline

### Lecture 1. Get to know your data

a. Overview of forecasting: how do you predict the future? What kind of accuracy is possible?
b. Where to obtain data; data sources at Fuqua and on the web
c. How to move data around: useful things you can do with your word processor and spreadsheet
d. What to look for in data: seasonality, inflation, trends, cycles, etc.
e. How to transform data to reveal its structure; deflation, logging, seasonal adjustment
f. Illustration of basic operations in Statgraphics

Reading assignment: Statgraphics Tutorial Introduction, H&R Chapters 1 and 3 (note: ignore details of how to use Minitab, SAS, and Forecast Plus throughout the book); also glance at pages 320-322 (how to use price indices) and pages 338-342 (how to compute seasonal indices), we'll return to these in lecture 3

### Lecture 2. Introduction to forecasting

a. Forecasting a stationary series: the "mean" model
b. Forecasting a nonstationary series I: the trend line model
c. Forecasting a nonstationary series II: the random walk ("naive") model
d. How to identify a random walk: differencing and autocorrelation analysis
e. Geometric random walk: the basic stock price model
f. Three types of forecasts: estimation period, validation period, and long-term extrapolation
g. How to evaluate forecast errors and compare models

Reading: Skim Chapter 2 and read Chapter 4, plus the first few pages of Chapter 5 (naive models) in H&R

### Lecture 3. Modeling of seasonality

HW#1 due
a. General considerations in working with seasonal data: causes of seasonality, stability of seasonal patterns
b. Seasonal random walk; and seasonal random trend models
c. Seasonal adjustment by the ratio-to-moving-average method
f. Trend/cycle decomposition of time series

Reading: Chapter 8 (decomposition of time series, calculation of seasonal indices)

### Lecture 4. Averaging and smoothing models

a. Simple moving average model
b. Exponential smoothing model
c. Combination of smoothing and seasonal adjustment
d. Robust models for noisy data
e. Massively parallel forecasting

Reading: Remainder of Chapter 5 (moving averages and smoothing methods)

### Lecture 5. Regression to mediocrity

HW#2 due
a. Indiana Jones and the temple of R-squared
b. Correlation matrices, autocorrelation and cross-correlation functions
c. Fitting simple regression models; interpreting output

### Lecture 6. Time series regression models

a. Fitting time series regression models

### Lecture 7. Regression continued

HW#3 due
a. What's a good value for R-squared?
b. Not-so-simple regression models

### Lecture 8. Regression wrapup

a. The four horsemen of linear regression
b. What's the bottom line?
c. Stepwise and all-subsets regression

Testing the assumptions of linear regression
What's the bottom line? How to compare models
Stepwise and all-possible-regressions

### Lecture 9. Introduction to ARIMA models

a. Naive + Autoregressive + Exponential Smoothing = ARIMA
b. Using ACF and PACF plots to determine the "signature" of a time series
c. Fitting non-seasonal ARIMA models
d. The spectrum of ARIMA models

### Lecture 10. ARIMA continued; seasonal models

HW#4 due
a. Identification of seasonal models
b. Examples of seasonal model-fitting

Estimation of ARIMA models
Seasonal ARIMA models

### Lecture 11. ARIMA wrapup, automation

Designated project" option (there is no HW#5)
a. Combination of ARIMA and regression models
b. Recap of steps in choosing a forecasting model
c. Automatic forecasting software

ARIMA models with regressors
Steps in choosing a forecasting model
Forecasting flow chart
Automatic forecasting software

### Lecture 12. Politics and ethics

a. Political and ethical issues in forecasting
b. Review of models: what to use and when