TIME SERIES ANALYSIS AND FORECASTING

First Edition (Revised: Sept. 2005)

Lon-Mu Liu, Ph.D

Table of Contents
Chapter 1. Introduction to Time Series Analysis and Forecasting

# Types of a time series# Applications of time series analysis
# Approaches for time series analysis and its applications# Model building and forecasting
# Principle of parsimony
# Automatic model identification approaches
# Evaluation of forecast performance
# Effects of outliers on forecast performance

Chapter 2. Autoregressive Integrated Moving Average Models

# Stationary time series and their characterization
# Autocorrelation function
# Partial autocorrelation function
# Extended autocorrelation function
# Stationary time series models and their characteristics
# Autoregressive, moving average, and mixed ARMA models
# Nonstationary time series models and their characteristics
# Model building
# Model identification
# Model estimation
# Diagnostic checking
# Forecasting
# Illustrative examples

Chapter 3. Seasonal ARIMA Models

# Attributes of seasonal time series
# Stationary seasonal models and their characteristics
# Pure seasonal autoregressive, moving average, and mixed ARMA models
# Nonstationary seasonal models and their characteristics
# Multiplicative and nonmultiplicative seasonal models
# Seasonal model identification
# Estimation of seasonal models
# Diagnostic checking of seasonal models
# Forecasting with seasonal models
# Illustrative examples

Chapter 4. ARIMA Modeling Using Expert Systems

# Two forms of ARIMA models
# Automatic identification of ARIMA models for nonseasonal time series
# Automatic identification of ARIMA models for seasonal time series
# Identification of seasonal ARIMA models using a filtering method
# Illustrative examples

Chapter 5. Transfer Function Models

# Relationship of transfer function models to regression models
# Multiple-input transfer function models
# Transfer function model identification using the LTF method
# Transfer function model estimation
# Diagnostic checking transfer function models
# Forecasting with transfer function models
# Illustrative examples

Chapter 6. Analysis of Time Series with Calendar Effects

# Trading day effects
# Holiday effects
# Modeling trading day effects using ARIMA models
# Modeling holiday effects using ARIMA models
# Identification of an ARIMA model for a time series with calendar effects
# Illustrative examples

Chapter 7. Intervention Analysis and Outlier Detection

# Characterizations for intervention effects
# Modeling strategies for intervention analysis
# Forecasting with an intervention model
# Outliers in time series and their types (AO, IO, LS, TC)
# Methods for outlier detection and adjustment
# An iterative procedure for joint estimation of model parameters and outlier effects
# Intervention analysis in the presence of outliers
# Forecasting in the presence of outliers
# Handling outliers at the end of a time series
# Handling missing data in a time series
# Illustrative examples

Chapter 8. Power Transformations and Forecasting

# Types of transformations for time series
# Power transformation and retransformation
# Effects of transformation on forecasts
# Debiasing forecasts in retransformation
# Procedures for searching the optimal power transformation in forecasting applications
# Remarks on power transformation
# Illustrative examples

Chapter 9. Time Series Data Mining

# Concepts in data mining
# Application of data mining concepts on time series analysis and forecasting
# The role of expert system time series modeling in data mining
# Time series data mining on electricity loads
# An example of time series data mining in business operations
# Methodology for data mining and knowledge discovery for time series
# Using automatic outlier detection methods as a tool for time series data mining

Chapter 10. Segmented Time Series Modeling and Forecasting

# Periodic grouping of data based on calendar and threshold values
# Threshold autogressive (TAR) models
# Multiplicative TAR models
# Multiplicative threshold ARIMA models
# Threshold transfer function models
# Handling clustered large outliers by discounting segments of data
# Illustrative examples

Chapter 11. Time Series Models with Heteroscedasticity

# Symmetric ARCH, GARCH, IGARCH, and GARCH-M models
# Asymmetric GJR-GARCH, EGARCH, and Threshold GARCH models
# Non-Normal error distributions based on student-t, Cauchy, and GED
# Measuring volatility leverage effects and risk premium
# Illustrative examples

Publisher: Scientific Computing Associates Corp.Copyright: 2005
Format: Paperback; 365 pp
Item #: 0-9765056-5-7
Price: $49.95 (*Price subject to change)