The late Søren Bisgaard, PhD, was professor of technology management at the University of Massachusetts Amherst. Throughout his esteemed career, Dr. Bisgaard made significant research contributions in the areas of experimental design, operations management, time series analysis, and Lean Six Sigma. A Fellow of the American Statistical Association and the American Society for Quality, he was also one of the cofounders of the European Network for Business and Industrial Statistics (ESBIS) in 1999. Dr. Bisgaard was awarded many honors for his achievements in the field of statistics, including the Brumbaugh Award (1988, 1996, and 2008), the Shewhart Medal (2002), the William G. Hunter Award (2002), and the George Box Award (2004). Murat Kulahci, PhD, is Associate Professor of Statistics in the Department of Informatics and Mathematical Modeling at the Technical University of Denmark. He has authored or coauthored over forty journal articles in the areas of time series analysis, design of experiments, and statistical process control and monitoring. Dr. Kulahci is coauthor of Introduction to Time Series Analysis and Forecasting (Wiley).

“It is a suitable text for courses on time series analysis at the (upper) undergraduate and graduate level. It can also serve as a guide for practitioners and researchers who carry out time series analysis in engineering, business and economics.”  (Zentralblatt MATH, 2012)"Time Series Analysis and Forecasting by Example is well recommended as a great introductory book for students transitioning from general statistics to time series as well as a good source book for intermediate level time series model builders." (Book Pleasures, 2012) "They set out to provide an introduction that is easy to understand and use, and that draws heavily from examples to demonstrate the principles and techniques." (Book News, 1 October 2011)

• Preface xi 1. Time Series Data: Examples and Basic Concepts 11.1 Introduction 11.2 Examples of Time Series Data 11.3 Understanding Autocorrelation 101.4 The World Decomposition 121.5 The Impulse Response Function 141.6 Superposition Principle 151.7 Parsimonious Models 18Exercises 192. Visualizing Time Series Data Structures: Graphical Tools 212.1 Introduction 212.2 Graphical Analysis of Time Series 222.3 Graph Terminology 232.4 Graphical Perception 242.5 Principles of Graph Construction 282.6 Aspect Ratio 302.7 Time Series Plots 342.8 Bad Graphics 38Exercises 463. Stationary Models 473.1 Basics of Stationary Time Series Models 473.2 Autoregressive Moving Average (ARMA) Models 543.3 Stationary and Invertibility of ARMA Models 623.4 Checking for Stationary using Variogram 663.5 Transformation of Data 69Exercises 734. Nonstationary Models 794.1 Introduction 794.2 Detecting Nonstationarity 794.3 Antoregressive Integrated Moving Average (ARIMA) Models 834.4 Forecasting using ARIMA Models 914.5 Example 2: Concentration Measurements from a Chemical Process 934.6 The EWMA Forecast 103Exercises 1045. Seasonal Models 1115.1 Seasonal Data 1115.2 Seasonal ARIMA Models 1165.3 Forecasting using Seasonal ARIMA Models 1245.4 Example 2: Company X’s Sales Data 126Exercises 1526. Time Series Model Selection 1556.1 Introduction 1556.2 Finding the “BEST” Model 1556.3 Example: Internet Users Data 1566.4 Model Selection Criteria 1636.5 Impulse Response Function to Study the Differences in Models 1666.6 Comparing Impulse Response Functions for Competing Models 1696.7 ARIMA Models as Rational Approximations 1706.8 AR Versus Arma Controversy 1716.9 Final Thoughts on Model Selection 173Appendix 6.1: How to Compute Impulse Response Functions with a Spreadsheet 173Exercises 1747. Additional Issues in ARIMA Models 1777.1 Introduction 1777.2 Linear Difference Equations 1777.3 Eventual Forecast Function 1837.4 Deterministic Trend Models 1877.5 Yet Another Argument for Differencing 1897.6 Constant Term in ARIMA Models 1907.7 Cancellation of Terms in ARIMA Models 1917.8 Stochastic Trend: Unit Root Nonstationary Processes 1947.9 Overdifferencing and Underdifferencing 1957.10 Missing Values in Time Series Data 197Exercises 2018. Transfer Function Models 2038.1 Introduction 2038.2 Studying Input-Output Relationships 2038.3 Example 1: The Box-Jenkins’ Gas Furnace 2048.4 Spurious Cross Correlations 2078.5 Prewhitening 2078.6 Identification of the Transfer Function 2138.7 Modeling the Noise 2158.8 The General Methodology for Transfer Function Models 2228.9 Forecasting Using Transfer Function-Noise Models 2248.10 Intervention Analysis 238Exercises 2619. Addition Topics 2639.1 Spurious Relationships 2639.2 Autocorrelation in Regression 2719.3 Process Regime Changes 2789.4 Analysis of Multiple Time Series 2859.5 Structural Analysis of Multiple Time Series 296Exercises 310Appendix A. Datasets Used in the Examples 311Appendix B. Datasets Used in the Exercises 327Bibliography 361Index 365