This title features an intuition-based approach that enables you to master time series analysis with ease. "Time Series Analysis and Forecasting by Example" provides the fundamental techniques in time series analysis using various examples. By introducing necessary theory through examples that showcase the discussed topics, the authors successfully help readers develop an intuitive understanding of seemingly complicated time series models and their implications. The book presents methodologies for time series analysis in a simplified, example-based approach. Using graphics, the authors discuss each presented example in detail and explain the relevant theory while also focusing on the interpretation of results in data analysis. Following a discussion of why autocorrelation is often observed when data is collected in time, subsequent chapters explore related topics, including: graphical tools in time series analysis; procedures for developing stationary, non-stationary, and seasonal models; how to choose the best time series model; constant term and cancellation of terms in ARIMA models; and, forecasting using transfer function-noise models. The final chapter is dedicated to key topics such as spurious relationships, autocorrelation in regression, and multiple time series. Throughout the book, real-world examples illustrate step-by-step procedures and instructions using statistical software packages such as SAS[registered], JMP, Minitab, SCA, and R. A related Web site features PowerPoint slides to accompany each chapter as well as the book's data sets. With its extensive use of graphics and examples to explain key concepts, "Time Series Analysis and Forecasting by Example" is an excellent book for courses on time series analysis at the upper-undergraduate and graduate levels. It also serves as a valuable resource for practitioners and researchers who carry out data and time series analysis in the fields of engineering, business, and economics
Chapter 1. Time Series Data: Examples and Basic Concepts. 1.1 Introduction. 1.2 Examples of time series data. 1.3 Understanding autocorrelation. 1.4 The Wold decomposition. 1.5 The impulse response function. 1.6 Superposition principle. 1.7 Parsimonious models. Chapter 2. Visualizing Time Series Data Structures: Graphical Tools. 2.1 Introduction. 2.2 Graphical Analysis of Time Series. 2.3 Graph terminology. 2.4 Graphical perception. 2.5 Principles of graph construction. 2.6 Aspect Ratio. 2.7 Time Series Plots. 2.8 Bad Graphics. Chapter 3. Stationary Models. 3.1 Basics of Stationary Time Series Models. 3.2 Autoregressive Moving Average (ARMA) models. 3.3 Stationary and Invertibility of ARMA models. 3.4 Checking for stability using variogram. 3.5 Transformation of data. Chapter 4. Nonstationary models. 4.1 Introduction. 4.2 Detecting nonstationarity. 4.3 Autoregressive Integrated Moving Average (ARIMA) models. 4.4 Forecasting using ARIMA models. 4.5 Example 2: Concentration Measures from a Chemical Process. 4.6 The EWMA forecast. Chapter 5. Seasonal Models. 5.1 Seasonal Data. 5.2 SEASONAL ARIMA MODELS. 5.3 Forecasting using Seasonal ARIMA models. 5.4 Example 2: Company X's Sales Data. Chapter 6. Time Series Model Selection. 6.1 Introduction. 6.2 Finding the "Best" Model. 6.3 Example: Internet Users Data. 6.4 Model Selection Criteria. 6.5 Impulse Response Function to study the differences in models. 6.6 Comparing Impulse response functions for competing models. 6.7 ARIMA models as rational approximations. 6.8 AR vs. ARMA Controversy. 6.9 Final Thoughts on model selection. Chapter 7. Additional Issues in ARIMA models. 7.1 Introduction. 7.2 Linear difference equations. 7.3 Eventual forecast function. 7.4 Deterministic trend models. 7.5 Yet another argument for differencing. 7.6 Constant term in ARIMA models. 7.7 Cancellation of terms in ARIMA models. 7.8 Stochastic trend: unit root nonstationary processes. 7.9 Overdifferencing and Underdifferencing. 7.10 Missing values in time series data. Chapter 8. Transfer Function Models. 8.1 Introduction. 8.2 Studying input-output relationships. 8.3 Example 1: Box-Jenkins' Gas Furnace. 8.4 Spurious cross correlations. 8.5 Prewhitening. 8.6 Identification of the transfer function. 8.7 Modeling the noise. 8.8 The general methodology for transfer function models. 8.9 Forecasting using transfer-noise models. 8.10 Intervention Analysis. Chapter 9. Additional Topics. 9.1 Spurious relationships. 9.2 Autocorrelation in Regression. 9.3 Process Regime Changes. 9.4 Analysis of multiple time series. 9.5 Structural Analysis of Multiple Time Series. Bibliography. Appendix A. Data Sets for Examples. Appendix B. Data Sets for Exercises.
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