Time series analysis and forecasting by example / / Soren Bisgaard, Murat Kulahci

• Record Nr.: UNINA9910137855203321
• Autore: Bisgaard Soren <1938-2009, >
• Pubbl/distr/stampa: Hoboken, New Jersey : , : Wiley, , [2010]; ©2010
• ISBN: 1-118-30288-5; 1-118-05695-7; 1-118-05694-9
• Descrizione fisica: 1 online resource (607 p.)
• Collana: Wiley series in probability and statistics
• Disciplina: 519.5/5; 519.55
• Soggetti: Time-series analysis; Forecasting; Electronic books.
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Description based upon print version of record.
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Cover; Series Page; Title Page; Copyright; Dedication Page; Preface; 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 GraphicsChapter 3: Stationary Models; 3.1 Basics of Stationary Time Series Models; 3.2 Autoregressive Moving Average (ARMA) Models; 3.3 Stationarity and Invertibility of ARMA Models; 3.4 Checking for Stationarity 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 Measurements from a Chemical Process; 4.6 The EWMA ; Forecast; Chapter 5: Seasonal Models; 5.1 Seasonal Data; 5.2 Seasonal Arima Models5.3 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 Versus Arma Controversy; 6.9 Final Thoughts on Model Selection; 6.10 Appendix 6.1: How to Compute Impulse Response Functions with a Spreadsheet; Chapter 7: Additional Issues in Arima Models7.1 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: The Box-Jenkins' Gas Furnace; 8.4 Spurious Cross Correlations; 8.5 Prewhitening8.6 Identification of the Transfer Function; 8.7 Modeling the Noise; 8.8 The General Methodology for Transfer Function Models; 8.9 Forecasting Using Transfer Function-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; Appendix A: Datasets used in the Examples; Appendix B: Datasets used in the Exercises; Bibliography; Wiley Series; Index
• Sommario/riassunto: An intuition-based approach 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 e