Introduction to Time Series Analysis and Forecasting. 2nd Edition. Wiley Series in Probability and Statistics

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1 Brochure More information from Introduction to Time Series Analysis and Forecasting. 2nd Edition. Wiley Series in Probability and Statistics Description: Praise for the First Edition " [t]he book is great for readers who need to apply the methods and models presented but have little background in mathematics and statistics." -MAA Reviews Thoroughly updated throughout, Introduction to Time Series Analysis and Forecasting, Second Edition presents the underlying theories of time series analysis that are needed to analyze time-oriented data and construct real-world short- to medium-term statistical forecasts. Authored by highly-experienced academics and professionals in engineering statistics, the Second Edition features discussions on both popular and modern time series methodologies as well as an introduction to Bayesian methods in forecasting. Introduction to Time Series Analysis and Forecasting, Second Edition also includes: - Over 300 exercises from diverse disciplines including health care, environmental studies, engineering, and finance - More than 50 programming algorithms using JMP, SAS, and R that illustrate the theory and practicality of forecasting techniques in the context of time-oriented data - New material on frequency domain and spatial temporal data analysis - Expanded coverage of the variogram and spectrum with applications as well as transfer and intervention model functions - A supplementary website featuring PowerPoint slides, data sets, and select solutions to the problems Introduction to Time Series Analysis and Forecasting, Second Edition is an ideal textbook upperundergraduate and graduate-levels courses in forecasting and time series. The book is also an excellent reference for practitioners and researchers who need to model and analyze time series data to generate forecasts. Contents: PREFACE xi 1 INTRODUCTION TO FORECASTING The Nature and Uses of Forecasts Some Examples of Time Series The Forecasting Process Data for Forecasting Resources for Forecasting 19 2 STATISTICS BACKGROUND FOR FORECASTING Introduction Graphical Displays Numerical Description of Time Series Data Use of Data Transformations and Adjustments General Approach to Time Series Modeling and Forecasting 61

2 2.6 Evaluating and Monitoring Forecasting Model Performance R Commands for Chapter REGRESSION ANALYSIS AND FORECASTING Introduction Least Squares Estimation in Linear Regression Models Statistical Inference in Linear Regression Prediction of New Observations Model Adequacy Checking Variable Selection Methods in Regression Generalized and Weighted Least Squares Regression Models for General Time Series Data Econometric Models R Commands for Chapter EXPONENTIAL SMOOTHING METHODS Introduction First Order Exponential Smoothing Modeling Time Series Data Second Order Exponential Smoothing Higher Order Exponential Smoothing Forecasting Exponential Smoothing for Seasonal Data Exponential Smoothing of Biosurveillance Data Exponential Smoothers and Arima Models R Commands for Chapter AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) MODELS Introduction Linear Models for Stationary Time Series Stationarity Stationary Time Series Finite Order Moving Average Processes Finite Order Autoregressive Processes Mixed Autoregressive Moving Average Processes 354

3 5.6 Nonstationary Processes Time Series Model Building Forecasting Arima Processes Seasonal Processes Arima Modeling of Biosurveillance Data Final Comments R Commands for Chapter TRANSFER FUNCTIONS AND INTERVENTION MODELS Introduction Transfer Function Models Transfer Function Noise Models Cross Correlation Function Model Specification Forecasting with Transfer Function Noise Models Intervention Analysis R Commands for Chapter SURVEY OF OTHER FORECASTING METHODS Multivariate Time Series Models and Forecasting Arch and Garch Models Direct Forecasting of Percentiles Combining Forecasts to Improve Prediction Performance Aggregation and Disaggregation of Forecasts Neural Networks and Forecasting Spectral Analysis Bayesian Methods in Forecasting Some Comments on Practical Implementation and Use of Statistical Forecasting Procedures R Commands for Chapter APPENDIX A STATISTICAL TABLES 561 APPENDIX B DATA SETS FOR EXERCISES 581 APPENDIX C INTRODUCTION TO R 627 BIBLIOGRAPHY 631

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