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

Additional lecture notes:

Famous forecasting quotes

How to shovel data around

Get to know your data

Inflation adjustment (deflation)

Seasonal adjustment

Stationarity and differencing

The logarithm transformation

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

Additional lecture notes:

Mean

Linear trend

Random walk

Random walk with growth

Geometric random walk

Three types of forecasts: estimation period, validation period, and the future

HOMEWORK ASSIGNMENT #1

*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

d. Additive versus multiplicative seasonal adjustment

e. Adjustments for holidays and trading days

f. Trend/cycle decomposition of time series

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

Addtional notes:

Seasonal differencing

Seasonal random walk

Seasonal random trend

Lecture 3 slides: working with seasonal data (revised Sep. 11)

Data set for assignment #2

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)

Additional notes:

Averaging and exponential smoothing models

Spreadsheet implementation of seasonal adjustment and exponential smoothing (revised 9/17)

*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

Reading: Chapter 6

Additional notes:

Introduction to regression analysis

Example of regression analysis: predicting auto sales from personal income

DATA SET FOR ASSIGNMENT #3 (FIXED 9/21)

NEW GRAPHICS DEFAULTS FOR STATGRAPHICS

a. Fitting time series regression models

Reading: Chapter 7

Additional notes:

Fitting time series regression models

What to look for in regression output

*HW#3 due
*a. What's a good value for R-squared?

b. Not-so-simple regression models

Reading: Chapter 9

Additional notes:

What's a good value for R-squared?

Not-so-simple regression models

DATA SET FOR ASSIGNMENT #4

a. The four horsemen of linear regression

b. What's the bottom line?

c. Stepwise and all-subsets regression

d. Business cycle indicators

Additional notes:

Testing the assumptions of linear regression

What's the bottom line? How to compare models

Stepwise and all-possible-regressions

Business Cycle Indicators

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

Reading: Chapter 10

Additional notes (revised 9/30):

Introduction to ARIMA: nonseasonal models

Identifying the order of differencing

Identifying the orders of AR or MA terms

Rules for identifying ARIMA models

*HW#4 due
*a. Identification of seasonal models

b. Examples of seasonal model-fitting

c. Spreadsheet implementation

Additional notes:

Estimation of ARIMA models

Seasonal ARIMA models

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

Additional notes:

ARIMA models with regressors

Steps in choosing a forecasting model

Forecasting flow chart

Automatic forecasting software

a. Political and ethical issues in forecasting

b. Review of models: what to use and when

Reading: Chapter 11

Political and ethical issues in forecasting

How to avoid trouble

Data transformations and forecasting models: what to use and when

GUIDELINES FOR FINAL PROJECTS

Notes concerning the final project (transforming, untransforming, comparing models, etc.)

*Last updated October 11, 1996. Always under construction.*