I. FOUNDATIONS OF FORECASTING1. Planning and Forecasting2. Statistical Fundamentals for ForecastingAppendix 2-A. Expected ValuesAppendix 2-B. Q-Statistic for White Noise ACF (k) s3. Introduction to Regression AnalysisSupplement 3: Cross Correlation FunctionsII. UNIVARIATE METHODS4. Simple Smoothing Methods5. Decomposition and Census II Methods6. Trend-Seasonal SmoothingSupplement 6: Fourier Series AnalysisIII. UNIVARIATE ARIMA METHODS7. ARIMA IntroductionAppendix 7-A. Useful Statistical DefinitionsAppendix 7-B. White Noise and StationarityAppendix 7-C. Theoretical ACFs for an ARIMA (1, 0, 0) ProcessAppendix 7-D. Theoretical ACFs for an ARIMA ( 0, 0, 1) ProcessAppendix 7-E. Checking Bounds of Invertibility and StationarityAppendix 7-F.Appendix 7-G. Partial Autocorrelations and the Yule- Walker Equations8. ARIMA Applications9. ARIMA Forecast ProfilesIV. MULTIVARIATE/CAUSAL METHODS10. Multiple Regression of Time SeriesAppendix 10-A. Deriving Normal Equations11. Econometric Methods12. ARIMA Intervention Analysis13. Multivariate ARIMA - Transfer FunctionsAppendix 13-A. Estimating Impulse Response WeightsV. CYCLICAL, QUALITATIVE, AND ARTIFICIAL INTELLIGENCE METHODS14. Cyclical Forecasting Methods14-A. Some General Theories Explaining Cycles15. Qualitative and Technological Forecasting Methods16. Expert Systems, Neural Networks, and Genetic AlgorithmsVI. COMBINING, VALIDATION, AND MANAGERIAL ISSUES17. Combining, Control and Validation Methods18. Method Characteristics, Accuracy, and Data SourcesAppendices
