Time Series Analysis And Forecasting By Example
商品資訊
ISBN13：9780470540640
出版社：John Wiley & Sons Inc
作者：Bisgaard
出版日：2011/06/29
裝訂／頁數：精裝／400頁
規格：24.8cm*15.2cm*2.5cm (高/寬/厚)
商品簡介
目次
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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