Introduction to time series analysis and forecasting

Abstract

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 CO22 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.