Introduction to time series analysis and forecasting / Douglas C. Montgomery, Cheryl L. Jennings, Murat Kulahci.

Previous edition: 2008.

Formerly CIP. Uk

Includes bibliographical references and index.

TOC <p>PREFACE xi <p>1 INTRODUCTION TO FORECASTING 1 <p>1.1 The Nature and Uses of Forecasts 1 <p>1.2 Some Examples of Time Series 6 <p>1.3 The Forecasting Process 13 <p>1.4 Data for Forecasting 16 <p>1.5 Resources for Forecasting 19 <p>2 STATISTICS BACKGROUND FOR FORECASTING 25 <p>2.1 Introduction 25 <p>2.2 Graphical Displays 26 <p>2.3 Numerical Description of Time Series Data 33 <p>2.4 Use of Data Transformations and Adjustments 46 <p>2.5 General Approach to Time Series Modeling and Forecasting 61 <p>2.6 Evaluating and Monitoring Forecasting Model Performance 64 <p>2.7 R Commands for Chapter 2 84 <p>3 REGRESSION ANALYSIS AND FORECASTING 107 <p>3.1 Introduction 107 <p>3.2 Least Squares Estimation in Linear Regression Models 110 <p>3.3 Statistical Inference in Linear Regression 119 <p>3.4 Prediction of New Observations 134 <p>3.5 Model Adequacy Checking 136 <p>3.6 Variable Selection Methods in Regression 146 <p>3.7 Generalized and Weighted Least Squares 152 <p>3.8 Regression Models for General Time Series Data 177 <p>3.9 Econometric Models 205 <p>3.10 R Commands for Chapter 3 209 <p>4 EXPONENTIAL SMOOTHING METHODS 233 <p>4.1 Introduction 233 <p>4.2 First-Order Exponential Smoothing 239 <p>4.3 Modeling Time Series Data 245 <p>4.4 Second-Order Exponential Smoothing 247 <p>4.5 Higher-Order Exponential Smoothing 257 <p>4.6 Forecasting 259 <p>4.7 Exponential Smoothing for Seasonal Data 277 <p>4.8 Exponential Smoothing of Biosurveillance Data 286 <p>4.9 Exponential Smoothers and Arima Models 299 <p>4.10 R Commands for Chapter 4 300 <p>5 AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) MODELS 327 <p>5.1 Introduction 327 <p>5.2 Linear Models for Stationary Time Series 328 <p>5.2.1 Stationarity 329 <p>5.2.2 Stationary Time Series 329 <p>5.3 Finite Order Moving Average Processes 333 <p>5.4 Finite Order Autoregressive Processes 337 <p>5.5 Mixed Autoregressive Moving Average Processes 354 <p>5.6 Nonstationary Processes 363 <p>5.7 Time Series Model Building 367 <p>5.8 Forecasting Arima Processes 378 <p>5.9 Seasonal Processes 383 <p>5.10 Arima Modeling of Biosurveillance Data 393 <p>5.11 Final Comments 399 <p>5.12 R Commands for Chapter 5 401 <p>6 TRANSFER FUNCTIONS AND INTERVENTION MODELS 427 <p>6.1 Introduction 427 <p>6.2 Transfer Function Models 428 <p>6.3 Transfer Function Noise Models 436 <p>6.4 Cross-Correlation Function 436 <p>6.5 Model Specification 438 <p>6.6 Forecasting with Transfer Function Noise Models 456 <p>6.7 Intervention Analysis 462 <p>6.8 R Commands for Chapter 6 473 <p>7 SURVEY OF OTHER FORECASTING METHODS 493 <p>7.1 Multivariate Time Series Models and Forecasting 493 <p>7.3 Arch and Garch Models 507 <p>7.4 Direct Forecasting of Percentiles 512 <p>7.5 Combining Forecasts to Improve Prediction Performance 518 <p>7.6 Aggregation and Disaggregation of Forecasts 522 <p>7.7 Neural Networks and Forecasting 526 <p>7.8 Spectral Analysis 529 <p>7.9 Bayesian Methods in Forecasting 535 <p>7.10 Some Comments on Practical Implementation and Use of Statistical Forecasting Procedures 542 <p>7.11 R Commands for Chapter 7 545 <p>APPENDIX A STATISTICAL TABLES 561 <p>APPENDIX B DATA SETS FOR EXERCISES 581 <p>APPENDIX C INTRODUCTION TO R 627 <p>BIBLIOGRAPHY 631 <p>INDEX 639

Summary:
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.

AS

Tahur Ahmed