Time Series Analysis and Forecasting Update 2/E 2009

        Time series analysis and forecasting have become increasingly important in many fields of research and application. The goal of this book is to distill and integrate current research results into cohesive and comprehensible methodologies, and to provide a streamlined approach to time series analysis and forecasting.
        This book is written in a manner which is suitable for a two-semester course in applied time series analysis and forecasting. It can be used in a one-semester course by excluding more advanced topics in the later chapters. The material is targeted to advanced undergraduate and graduate level students in various fields of study. With its emphasis on practicality and applicability of methodologies, the book is also useful as a reference source for researchers and practitioners in time series analysis and forecasting. In addition to traditional Box-Jenkins methodology, this book places greater emphasis on these newer issues, such as modeling automation, outliers, heteroscedasticity, and nonlinearity , as well as multivariate approaches in time series analysis and forecasting. More details regarding the contents of the book can be found in http://www.scausa.com/toc.htm .

Ch01 Introduction
1.1 Time Series Data
1.2 Application of Time Series Analysis
1.3 Approaches for Time Series Analysis and Its Applications
1.4 Model Building and Forecasting
1.5 Evaluation of Forecast Performance
1.6 Outline of This Book

Ch02 Autoregressive Integrated Moving Average Models
2.1 Stationary Time Series and Their Characterization
2.2 Important Notations
2.3 Stationary Time Series Models and Their Characteristics
2.4 Partial Autocorrelation Function(PACF)
2.5 PACF for Stationary Time Series Models
2.6 Extended Autocorrelation function(EACF)
2.7 Sample EACF for Stationary Time Series Models
2.8 Nonstationary Models
2.9 Model Building
2.10 Model Identification
2.11 Model Estimation
2.12 Diagnostic Checking
2.13 Forecasting
2.14 Additional Example

Ch03 Seasonal Arima Models
3.1 Seasonal Time Series Data and Notations
3.2 Stationary Seasonal Time Series Models and Their Characteristics
3.3 PACF and EACF for Stationary Seasonal Time Series Models
3.4 Nonstationary Seasonal Models and Their Characteristics
3.5 Multiplicative and Nonmultiplicative Seasonal Models
3.6 Model Identification
3.7 Model Estimation
3.8 Diagnostic Checking
3.9 Forecasting
3.10 Illustrative Examples

Ch04 Arima Modeling Using Expert Systems
4.1 The Univariate ARIMA Model
4.2 Automatic Identification of ARIMA Models for Nonseasonal Time Series
4.3 Identification of Seasonal ARIMA Models Using a Filtering Method
4.4 An Example: Quarterly Nominal GNP of the United States
4.5 Additional Examples and Simulation Studies

Ch05 Transfer Function Models
5.1 Transfer Function Models
5.2 Transfer Function Model Identification
5.3 Transfer Function Model Estimation
5.4 Diagnostic Checking of an Estimated Transfer Function Model
5.5 Forecasting
5.6 Additional Illustrative Examples

Ch06 Analysis of Time Series with Calendar Effects
6.1 ARIMA Models with Calendar Variation
6.2 Identification of ARIMA Models of the Disturbance Term
6.3 An Example of Model Identification in the Presence of the Trading Day Effects
6.4 An Example of Model Identification in the Presence of the Holiday Effects
6.5 Discussion

Ch07 Intervention Analysis and Outlier Detection
7.1 Models for Intervention Analysis
7.2 Examples of Invention Analysis
7.3 Forecasting with an Intervention
7.4 Outliers in a Time Series
7.5 Methods for Outlier Detection and Adjustment
7.6 An Example for Joint Estimation of Outlier Effects and Model Parameters
7.7 Intervention Analysis in the Presence of Outliers
7.8 Forecasting in the Presence of Outliers
7.9 Modeling and Forecasting Time Series in the Presence of Missing Observations

Ch08 Forecasting Using Exponential Smoothing Methods
8.1 The Naïve Methods
8.2 The Averaging Methods
8.3 Simple(Single)Exponential Smoothing
8.4 Double Exponential Smoothing
8.5 Holt’s Two Parameter Exponential Smoothing
8.6 Winters’ Additive Seasonal Exponential Smoothing Method
8.7 Winters’ Multiplicative Seasonal Exponential Smoothing Method
8.8 General Exponential Smoothing Using Seasonal Indicators
8.9 General Exponential Smoothing Using Harmonic Functions
8.10 Forecasting Using Exponential Smoothing Methods in Comparison to ARIMA Modeling

Ch09 Time Series Data Mining
9.1 Data Mining
9.2 Time Series Data Mining on Electricity Loads
9.3 An Example of Business Operation and Data Mining Applications
9.4 Methodology for Data Mining and Knowledge Discovery in Time Series
9.5 Data Mining at the Corporate Level and Its Applications
9.6 Discussion

Ch10 Power Transformation and Forecasting
10.1 Power Transformation of a Time Series
10.2 Retransformation of power Transformed Forecasts
10.3 Procedures for Searching a Power Transformation
10.4 Remarks on Power Transformation

Ch11 Time Series Models with Heteroscedasticity
11.1 An Example of Time Series with Heteroscedasticity
11.2 The ARCH Model
11.3 The GARCH Model
11.4 Building an ARCH/GARCH Model
11.5 The Integrated GARCH(IGARCH) Model
11.6 The GARCH-M Model
11.7 Asymmetric Garch Models
11.8 The Conditional Heteroscedastic ARMA(CHARMA) Model
11.9 The Stochastic Volatility(SV) Model
11.10 Regression Plus GARCH Extension

Ch12 Segmented Time Series Modeling and Forecasting
12.1 Model Estimation Using Weighted Method
12.2 Modeling and Forecasting Seasonal Time Series
12.3 Modeling and Forecasting Daily and Hourly Time Series
12.4 Threshold Transfer Function Models
12.5 Threshold Autoregressive Modeling and Forecasting

Ch13 Nonlinear Time Series Models
13.1 Threshold Autoregressive Models
13.2 Nonlinearity Test
13.3 Identification of Threshold Values

Ch14 Multivariate Time Series Analysis and Forecasting Using Vector ARMA Models
14.1 Vector ARMA Models
14.2 Examples of Vector Models
14.3 Relationship between Vector ARMA Models and Other Time Series Models
14.4 Characteristics of Some Vector ARMA Models
14.5 Partial Autoregression Matrices in Vector Models
14.6 Extended Cross Correlation Matrices(ECCM)
14.7 Building Vector Models
14.8 Forecasting Using Vector ARMA Models
14.9 Multiplicative Seasonal Vector ARMA Models
14.10 Analysis of Actual Time Series
14.11 Eigenvalue-Eigenvector Analysis in Multivariate Time Series
14.12 Alternative Approach to Modeling Multiple Time Series

Ch15 Multivariate Time Series Analysis and Forecasting Using Simultaneous Transfer Function Models
15.1 Simultaneous Transfer Function Models
15.2 Structural Form and Reduced Form Models
15.3 Model Building Strategy for Reduced Form STF Models
15.4 Model Building Strategy for Structural Form STF Model
15.5 Additional Illustrated Examples
15.6 Multivariate Time Series Analysis and Forecasting with Interventions
15.6 Econometric Modeling Using STF Models

Ch16 Causality Analysis
16.1 Dynamic Relationship between Economic Time Series
16.2 A Decision Tree Approach for Detecting Dynamic Relationship
16.3 Illustrative Examples
16.4 Other Procedures for Causality Testing