Introduction to Time Series Analysis and Forecasting, Solutions ManualISBN: 9780470435748
88 pages
March 2009

Analyzing timeoriented data and forecasting are among the most important problems that analysts face across many fields, ranging from finance and economics to production operations and the natural sciences. As a result, there is a widespread need for large groups of people in a variety of fields to understand the basic concepts of time series analysis and forecasting. Introduction to Time Series Analysis and Forecasting presents the time series analysis branch of applied statistics as the underlying methodology for developing practical forecasts, and it also bridges the gap between theory and practice by equipping readers with the tools needed to analyze timeoriented data and construct useful, short to mediumterm, statistically based forecasts.
Seven easytofollow chapters provide intuitive explanations and indepth coverage of key forecasting topics, including:

Regressionbased methods, heuristic smoothing methods, and general time series models

Basic statistical tools used in analyzing time series data

Metrics for evaluating forecast errors and methods for evaluating and tracking forecasting performance over time

Crosssection and time series regression data, least squares and maximum likelihood model fitting, model adequacy checking, prediction intervals, and weighted and generalized least squares

Exponential smoothing techniques for time series with polynomial components and seasonal data

Forecasting and prediction interval construction with a discussion on transfer function models as well as intervention modeling and analysis

Multivariate time series problems, ARCH and GARCH models, and combinations of forecasts
The ARIMA model approach with a discussion on how to identify and fit these models for nonseasonal and seasonal time series
The intricate role of computer software in successful time series analysis is acknowledged with the use of Minitab, JMP, and SAS software applications, which illustrate how the methods are implemented in practice. An extensive FTP site is available for readers to obtain data sets, Microsoft Office PowerPoint slides, and selected answers to problems in the book. Requiring only a basic working knowledge of statistics and complete with exercises at the end of each chapter as well as examples from a wide array of fields, Introduction to Time Series Analysis and Forecasting is an ideal text for forecasting and time series courses at the advanced undergraduate and beginning graduate levels. The book also serves as an indispensable reference for practitioners in business, economics, engineering, statistics, mathematics, and the social, environmental, and life sciences.
Preface ix
1. Introduction to Forecasting 1
1.1 The Nature and Uses of Forecasts, 1
1.2 Some Examples of Time Series, 5
1.3 The Forecasting Process, 12
1.4 Resources for Forecasting, 14
2. Statistics Background for Forecasting 18
2.1 Introduction, 18
2.2 Graphical Displays, 19
2.3 Numerical Description of Time Series Data, 25
2.4 Use of Data Transformations and Adjustments, 34
2.5 General Approach to Time Series Modeling and Forecasting, 46
2.6 Evaluating and Monitoring Forecasting Model Performance, 49
3. Regression Analysis and Forecasting 73
3.1 Introduction, 73
3.2 Least Squares Estimation in Linear Regression Models, 75
3.3 Statistical Inference in Linear Regression, 84
3.4 Prediction of New Observations, 96
3.5 Model Adequacy Checking, 98
3.6 Variable Selection Methods in Regression, 106
3.7 Generalized and Weighted Least Squares, 111
3.8 Regression Models for General Time Series Data, 133
4. Exponential Smoothing Methods 171
4.1 Introduction, 171
4.2 FirstOrder Exponential Smoothing, 176
4.3 Modeling Time Series Data, 180
4.4 SecondOrder Exponential Smoothing, 183
4.5 HigherOrder Exponential Smoothing, 193
4.6 Forecasting, 193
4.7 Exponential Smoothing for Seasonal Data, 210
4.8 Exponential Smoothers and ARIMA Models, 217
5. Autoregressive Integrated Moving Average (ARIMA) Models 231
5.1 Introduction, 231
5.2 Linear Models for Stationary Time Series, 231
5.3 Finite Order Moving Average (MA) Processes, 235
5.4 Finite Order Autoregressive Processes, 239
5.5 Mixed Autoregressive–Moving Average (ARMA) Processes, 253
5.6 Nonstationary Processes, 256
5.7 Time Series Model Building, 265
5.8 Forecasting ARIMA Processes, 275
5.9 Seasonal Processes, 282
5.10 Final Comments, 286
6. Transfer Functions and Intervention Models 299
6.1 Introduction, 299
6.2 Transfer Function Models, 300
6.3 Transfer Function–Noise Models, 307
6.4 Cross Correlation Function, 307
6.5 Model Specification, 309
6.6 Forecasting with Transfer Function–Noise Models, 322
6.7 Intervention Analysis, 330
7. Survey of Other Forecasting Methods 343
7.1 Multivariate Time Series Models and Forecasting, 343
7.2 State Space Models, 350
7.3 ARCH and GARCH Models, 355
7.4 Direct Forecasting of Percentiles, 359
7.5 Combining Forecasts to Improve Prediction Performance, 365
7.6 Aggregation and Disaggregation of Forecasts, 369
7.7 Neural Networks and Forecasting, 372
7.8 Some Comments on Practical Implementation and Use of Statistical Forecasting Procedures, 375
Appendix A. Statistical Tables 387
Appendix B. Data Sets for Exercises 407
Bibliography 437
Index 443
Cheryl L. Jennings, PhD, is a Process Design Consultant with Bank of America. An active member of both the American Statistical Association and the American Society for Quality, her areas of research and professional interest include Six Sigma; modeling and analysis; and process control and improvement. Dr. Jennings earned her PhD in industrial engineering from Arizona State University.
Murat Kulahci, PhD, is Associate Professor in Informatics and Mathematical Modelling at the Technical University of Denmark. He has authored or coauthored over thirty journal articles in the areas of time series analysis, design of experiments, and statistical process control and monitoring.