FTP site with data sets and presentation slides
Solutions Manual
CODE
R code for various examples can be found under the Time Series page.
Table of Contents
1. Introduction to Forecasting
1.1 The Nature and uses of Forecasts
1.2 Some Examples of Time Series
1.3 The Forecasting Process
1.4 Resources for Forecasting
2. Statistics Background for Forecasting
2.1 Introduction
2.2 Graphical Displays
2.3 Numerical Description of Time Series Data
2.4 Use of Data Transformations and Adjustments
2.5 General Approach to Time Series Analysis and Forecasting
2.6 Evaluating and Monitoring Forecasting Model Performance
3. Regression Analysis and Forecasting
3.1 Introduction
3.2 Least Squares Estimation in Linear Regression Models
3.3 Statistical Inference in Linear Regression
3.4 Prediction of New Observations
3.5 Model Adequacy Checking
3.6 Variable Selection Methods in Regression
3.7 Generalized and Weighted Least Squares
3.8 Regression Models for General Time Series Data
4. Exponential Smoothing Methods
4.1 Introduction
4.2 First-Order Exponential Smoothing
4.3 Modeling Time series Data
4.4 Second-Order Exponential Smoothing
4.5 Higher-Order Exponential Smoothing
4.6 Forecasting
4.7 Exponential Smoothing for Seasonal Data
4.8 Exponential Smoothers and ARIMA Models
5. Autoregressive Integrated Moving Average (ARIMA) Models
5.1 Introduction
5.2 Linear Models for Stationary Time Series
5.3 Finite Order Moving Average (MA) Processes
5.4 Finite Order Autoregressive Processes
5.5 Mixed Autoregressive-Moving Average (ARMA) Processes
5.6 Non-stationary Processes
5.7 Time Series Model Building
5.8 Forecasting ARIMA Processes
5.9 Seasonal Processes
5.10 Final Comments
6. Transfer Function and Intervention Models
6.1 Introduction
6.2 Transfer Function Models
6.3 Transfer Function-Noise Models
6.4 Cross Correlation Function
6.5 Model Specification
6.6 Forecasting with Transfer Function-Noise Models
6.7 Intervention Analysis
7. Survey of Other Forecasting Methods
7.1 Multivariate Time Series Models and Forecasting
7.2 State Space Models
7.3 ARCH and GARCH Models
7.4 Direct Forecasting of Percentiles
7.5 Combining Forecasts to Improve Prediction Performance
7.6 Aggregation and Disaggregation of Forecasts
7.7 Neural Networks and Forecasting
7.8 Some Comments on Practical Implementation and use of Statistical Forecasting Techniques
Bibliography
Appendix
Appendix A Statistical Tables
Table A.1 Cumulative Normal Distribution
Table A.2 Percentage Points of the Chi-Square Distribution
Table A.3 Percentage Points of the t Distribution
Table A.4 Percentage Points of the F Distribution
Table A.5 Critical Values of the Durbin-Watson Statistic
Appendix B Data Sets for Exercises
Table B.1 Market Yield on U.S. Treasury Securities at 10-year Constant Maturity
Table B.2 Pharmaceutical Product Sales
Table B.3 Chemical Process Viscosity
Table B.4 U.S Production of Blue and Gorgonzola Cheeses
Table B.5 U.S. Beverage Manufacturer Product Shipments, Unadjusted
Table B.6 Global Mean Surface Air Temperature Anomaly and Global CO2<sub>2</sub> Concentration
Table B.7 Whole Foods Market Stock Price, Daily Closing Adjusted for Splits
Table B.8 Unemployment Rate - Full-Time Labor Force, Not Seasonally Adjusted
Table B.9 International Sunspot Numbers
Table B.10 United Kingdom Airline Miles Flown
Table B.11 Champagne Sales
Table B.12 Chemical Process Yield, with Operating Temperature (Uncontrolled)
Table B.13 U.S. Production of Ice Cream and Frozen Yogurt
Table B.14 Atmospheric CO2 Concentrations at Mauna Loa Observatory
Table B.15 U.S. National Violent Crime Rate
Table B.16 U.S. Gross Domestic Product
Table B.17 U.S. Total Energy Consumption
Table B.18 U.S. Coal Production
Table B.19 Arizona Drowning Rate, Children 1-4 Years Old
Table B.20 U.S. Internal Revenue Tax Refunds
Index