Introduction to Time Series Analysis and Forecasting

ISBN-10: 0471653977

ISBN-13: 9780471653974

Edition: 2008

List price: $157.00
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Introduction to Time Series Analysis and Forecasting examines methods for modeling and analyzing time series data with a view towards drawing inferences about the data and generating forecasts that will be useful to the decision maker. While the level is advanced undergraduate/first-year graduate, with a prerequisite knowledge of basic statistical methods, some portions of the book require a first course in calculus and modest matrix algebra manipulation skills. Minitab and SAS Software System are used extensively to illustrate how the methods in the text are implemented in practice.
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Book details

List price: $157.00
Copyright year: 2008
Publisher: John Wiley & Sons, Incorporated
Publication date: 3/28/2008
Binding: Hardcover
Pages: 472
Size: 6.00" wide x 8.00" long x 0.75" tall
Weight: 1.980
Language: English

Introduction to Forecasting
The Nature and Uses of Forecasts
Some Examples of Time Series
The Forecasting Process
Resources for Forecasting
Statistics Background for Forecasting
Graphical Displays
Time Series Plots
Plotting Smoothed Data
Numerical Description of Time Series Data
Stationary Time Series
Autocovariance and Autocorrelation Functions
Use of Data Transformations and Adjustments
Trend and Seasonal Adjustments
General Approach to Time Series Modeling and Forecasting
Evaluating and Monitoring Forecasting Model Performance
Forecasting Model Evaluation
Choosing Between Competing Models
Monitoring a Forecasting Model
Regression Analysis and Forecasting
Least Squares Estimation in Linear Regression Models
Statistical Inference in Linear Regression
Test for Significance of Regression
Tests on Individual Regression Coefficients and Groups of Coefficients
Confidence Intervals on Individual Regression Coefficients
Confidence Intervals on the Mean Response
Prediction of New Observations
Model Adequacy Checking
Residual Plots
Scaled Residuals and PRESS
Measures of Leverage and Influence
Variable Selection Methods in Regression
Generalized and Weighted Least Squares
Generalized Least Squares
Weighted Least Squares
Discounted Least Squares
Regression Models for General Time Series Data
Detecting Autocorrelation: The Durbin-Watson Test
Estimating the Parameters in Time Series Regression Models
Exponential Smoothing Methods
First-Order Exponential Smoothing
The Initial Value, y[subscript 0]
The Value of [lambda]
Modeling Time Series Data
Second-Order Exponential Smoothing
Higher-Order Exponential Smoothing
Constant Process
Linear Trend Process
Estimation of [sigma subscript e superscript 2]
Adaptive Updating of the Discount Factor
Model Assessment
Exponential Smoothing for Seasonal Data
Additive Seasonal Model
Multiplicative Seasonal Model
Exponential Smoothers and ARIMA Models
Autoregressive Integrated Moving Average (ARIMA) Models
Linear Models for Stationary Time Series
Stationary Time Series
Finite Order Moving Average (MA) Processes
The First-Order Moving Average Process, MA(1)
The Second-Order Moving Average Process, MA(2)
Finite Order Autoregressive Processes
First-Order Autoregressive Process, AR(1)
Second-Order Autoregressive Process, AR(2)
General Autoregressive Process, AR(p)
Partial Autocorrelation Function, PACF
Mixed Autoregressive-Moving Average (ARMA) Processes
Nonstationary Processes
Time Series Model Building
Model Identification
Parameter Estimation
Diagnostic Checking
Examples of Building ARIMA Models
Forecasting ARIMA Processes
Seasonal Processes
Final Comments
Transfer Functions and Intervention Models
Transfer Function Models
Transfer Function-Noise Models
Cross Correlation Function
Model Specification
Forecasting with Transfer Function-Noise Models
Intervention Analysis
Survey of Other Forecasting Methods
Multivariate Time Series Models and Forecasting
Multivariate Stationary Process
Vector ARIMA Models
Vector AR (VAR) Models
State Space Models
ARCH and GARCH Models
Direct Forecasting of Percentiles
Combining Forecasts to Improve Prediction Performance
Aggregation and Disaggregation of Forecasts
Neural Networks and Forecasting
Some Comments on Practical Implementation and Use of Statistical Forecasting Procedures
Statistical Tables
Data Sets for Exercises
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