Bibliographic record and links to related information available from the Library of Congress catalog.
Note: Contents data are machine generated based on pre-publication provided by the publisher. Contents may have variations from the printed book or be incomplete or contain other coding.
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 2.2.1 Time Series Plots 2.2.2 Plotting Smoothed Data 2.3 Numerical Description of Time Series Data 2.3.1 Stationary Time series 2.3.2 Autocovariance and Autocorrelation Functions 2.4 Use of Data Transformations and Adjustments 2.4.1 Transformations 2.4.2 Trend and Seasonal Adjustments 2.5 General Approach to Time Series Analysis and Forecasting 2.6 Evaluating and Monitoring Forecasting Model Performance 2.6.1 Forecasting Model Evaluation 2.6.2 Choosing Between Competing Models 2.6.3 Monitoring a Forecasting Model 3. Regression Analysis and Forecasting 3.1 Introduction 3.2 Least Squares Estimation in Linear Regression Models 3.3 Statistical Inference in Linear Regression 3.3.1 Test for Significance of Regression 3.3.2 Tests on Individual Regression Coefficients and Groups of Coefficients 3.3.3 Confidence Intervals on Individual Regression Coefficients 3.3.4 Confidence Intervals on the Mean Response 3.4 Prediction of New Observations 3.5 Model Adequacy Checking 3.5.1 Residual Plots 3.5.2 Scaled Residuals and PRESS 3.5.3 Measures of Leverage and Influence 3.6 Variable Selection Methods in Regression 3.7 Generalized and Weighted Least Squares 3.7.1 Generalize Least Squares 3.7.2 Weighted Least Squares 3.7.3 Discounted Least Squares 3.8 Regression Models for General Time Series Data 3.8.1 Detecting Autocorrelation: The Durbin-Watson Test 3.8.2 Estimating the Parameters in Time Series Regression Models 4 Exponential Smoothing Methods 4.1 Introduction 4.2 First-Order Exponential Smoothing 4.2.1 The Initial Value, 4.2.2 The Value of 4.3 Modeling Time series Data 4.4 Second-Order Exponential Smoothing 4.5 Higher-Order Exponential Smoothing 4.6 Forecasting 4.6.1 Constant Process 4.6.2 Linear Trend process 4.6.3 Estimation of 4.6.4 Adaptive Updating of the Discount Factor 4.6.5 Model Assessment 4.7 Exponential Smoothing for Seasonal Data 4.7.1 Additive Seasonal Model 4.7.2 Multiplicative Seasonal Model 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 5.2.1 Stationarity 5.2.2 Stationary Time Series 5.3 Finite Order Moving Average (MA) Processes 5.3.1 The First-Order Moving Average Process, MA(1) 5.3.2 The Second-Order Moving Average Process, MA(2) 5.4 Finite Order Autoregressive Processes 5.4.1 First Order Autoregressive Process, AR(1) 5.4.2 Second Order Autoregressive Process, AR(2) 5.4.3 General Autoregressive Process, AR(p) 5.4.4 Partial Autocorrelation Function, PACF 5.5 Mixed Autoregressive-Moving Average (ARMA) Processes 5.6 Non-stationary Processes 5.7 Time Series Model Building 5.7.1 Model Identification 5.7.2 Parameter Estimation 5.7.3 Diagnostic Checking 5.7.4 Examples of Building ARIMA Models 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 7.1.1 Multivariate Stationary Process 7.1.2 Vector ARIMA Processes 7.1.3 Vector AR (VAR) Models 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 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
Library of Congress Subject Headings for this publication:
Time-series analysis.
Forecasting.