Business Forecasting with Accompanying Excel-Based ForecastXtm Software 4th Edition, 2002
Forecasting & Planning
by Barry & J. Holton Wilson Keating

1. Introduction to Business Forecasting

  • Introduction
  • Quantitative Forecasting Has Become Widely Accepted
  • Forecasting in Business Today
  • Forecasting in the Public and Not-for-Profit Sectors
  • Forecasting and Supply Chain Management
  • Computer Use and Quantitative Forecasting
  • Subjective Forecasting Methods
  • New-Product Forecasting
  • Two Simple Naïve Models
  • Evaluating Forecasts
  • Using Multiple Forecasts
  • Sources of Data
  • Forecasting Domestic Car Sales
  • Overview of the Text
  • Integrative Case: Forecasting Sales of The Gap
  • Case Questions
  • Solutions to Case Questions
  • Case References
  • About ForecastXtm: The Software on the CD with Your Business Forecasting with Accompanying Excel-Based ForecastXtm Software Text
  • Getting Started: ProCasttm Jump Starts Your Forecasting Process
  • Suggested Readings and Web Sites
  • Exercises


2. The Forecast Process, Data Considerations, and Model Selection

  • Introduction
  • The Forecast Process
  • Trend, Seasonal, and Cyclical Data Patterns
  • Data Patterns and Model Selection
  • A Statistical Review
  • Correlograms: An Alternative Method of Data Exploration
  • Domestic Car Sales: Exploratory Data Analysis and Model Selection
  • Integrative Case: The Gap
  • Case Questions
  • Solutions to Case Questions
  • Using ForecastXtm to Find Autocorrelation Functions
  • Suggested Readings
  • Exercises


3. Moving Averages and Exponential Smoothing

  • Moving Averages
  • Simple Exponential Smoothing
  • Holt’s Exponential Smoothing
  • Winters’ Exponential Smoothing
  • Adaptive--Response-Rate Single Exponential Smoothing
  • Using Single, Holt’s, or ADRES Smoothing to Forecast a Seasonal Data Series
  • Event Modeling
  • Summary
  • Forecasting Domestic Car Sales with Exponential Smoothing
  • Integrative Case: The Gap
  • Case Questions
  • Solutions to Case Questions
  • Using ForecastXtm to Make Exponential Smoothing Forecasts
  • Suggested Readings
  • Exercises


4. Introduction to Forecasting with Regression Methods

  • The Bivariate Regression Model
  • Visualization of Data: An Important Step in Regression Analysis
  • A Process for Regression Forecasting
  • Forecasting with a Simple Linear Trend
  • Using a Causal Regression Model to Forecast
  • A Retail Sales Forecast Based on Disposable Personal Income Per Capita
  • A Retail Sales Forecast Based on the Mortgage Rate
  • Statistical Evaluation of Regression Models
  • Using the Standard Error of the Estimate
  • Heteroscedasticity
  • Cross-Sectional Forecasting
  • Forecasting Domestic Car Sales with Bivariate Regression
  • Integrative Case: The Gap
  • Case Questions
  • Solutions to Case Questions
  • Using ForecastXtm to Make Regression Forecasts
  • Further Comments on Regression Models
  • Suggested Readings
  • Exercises


5. Forecasting with Multiple Regression

  • The Multiple-Regression Model
  • Selecting Independent Variables
  • Forecasting with a Multiple-Regression Model
  • Statistical Evaluation of Multiple-Regression Models
  • Serial Correlation and the Omitted-Variable Problem
  • Accounting for Seasonality in a Multiple-Regression Model
  • Extensions of the Multiple-Regression Model
  • Advice on Using Multiple-Regression in Forecasting
  • Forecasting Domestic Car Sales with Multiple Regression
  • Integrative Case: The Gap
  • Case Questions
  • Solutions to Case Questions
  • Using ForecastXtm to Make Multiple-Regression Forecasts
  • Suggested Readings
  • Exercises


6. Time-Series Decomposition

  • The Basic Time-Series Decomposition Model
  • Deseasonalizing the Data and Finding Seasonal Indexes
  • Finding the Long-Term Trend
  • Measuring the Cyclical Component
  • The Time-Series Decomposition Forecast
  • Forecasting Domestic Car Sales by Using Time-Series Decomposition
  • Integrative Case: The Gap
  • Case Questions
  • Solutions to Case Questions
  • Using ForecastXtm to Make Time-Series Decomposition Forecasts
  • Suggested Readings
  • Exercises
  • Appendix: Components of the Composite Indexes


7. ARIMA (Box-Jenkins)-Type Forecasting Models

  • Introduction
  • The Philosophy of Box-Jenkins
  • Moving-Average Models
  • Autoregression Models
  • Mixed Autoregression and Moving-Average Models
  • Stationarity
  • The Box-Jenkins Identification ProcessARIMA: A Set of Numerical Examples
  • Forecasting Seasonal Time Series
  • Domestic Car Sales
  • Integrative Case: Forecasting Sales of The Gap
  • Case Questions
  • Solutions to Case Questions
  • Using ForecastXtm to Make ARIMA (Box-Jenkins) Forecasts
  • Suggested Readings
  • Exercises
  • Appendix: Critical Values of Chi-Square


8. Combining Forecast Results

  • Introduction
  • Bias
  • An Example
  • What Kinds of Forecasts Can Be Combined?
  • Considerations in Choosing the Weights for Combined Forecasts
  • Three Techniques for Selecting Weights When Combining Forecasts
  • Forecasting Domestic Car Sales with a Combined Forecast
  • Integrative Case: The Gap
  • Case Questions
  • Solutions to Case Questions
  • Using ForecastXtm to Combine Forecasts
  • Suggested Readings
  • Exercises


9. Forecast Implementation

  • Keys to Obtaining Better Forecasts
  • The Forecast Process
  • Choosing the Right Forecasting Techniques
  • New Product Forecasting
  • Artificial Intelligence and Forecasting
  • Summary
  • Using “ProCasttm” in ForecastXtm to Make Forecasts
  • Suggested Readings
  • Exercises
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