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Time series analysis and forecasting by example / / Soren Bisgaard, Murat Kulahci
| Time series analysis and forecasting by example / / Soren Bisgaard, Murat Kulahci |
| Autore | Bisgaard Soren <1938-2009, > |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : Wiley, , [2010] |
| Descrizione fisica | 1 online resource (607 p.) |
| Disciplina |
519.5/5
519.55 |
| Collana | Wiley series in probability and statistics |
| Soggetto topico |
Time-series analysis
Forecasting |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-118-30288-5
1-118-05695-7 1-118-05694-9 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| 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 |
| Record Nr. | UNINA-9910137855203321 |
Bisgaard Soren <1938-2009, >
|
||
| Hoboken, New Jersey : , : Wiley, , [2010] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Time series analysis and forecasting by example / / Soren Bisgaard, Murat Kulahci
| Time series analysis and forecasting by example / / Soren Bisgaard, Murat Kulahci |
| Autore | Bisgaard Soren <1938-2009, > |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : Wiley, , [2010] |
| Descrizione fisica | 1 online resource (607 p.) |
| Disciplina |
519.5/5
519.55 |
| Collana | Wiley series in probability and statistics |
| Soggetto topico |
Time-series analysis
Forecasting |
| ISBN |
1-118-30288-5
1-118-05695-7 1-118-05694-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| 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 |
| Record Nr. | UNINA-9910830183603321 |
|
Bisgaard Soren <1938-2009, >
|
||
| Hoboken, New Jersey : , : Wiley, , [2010] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||