Introduction to Time Series Analysis and Forecasting

Name: Introduction to Time Series Analysis and Forecasting
Price: 22.5 USD
Availability: InStock
ISBN: 9780471653974

ISBN-10: 0471653977

ISBN-13: 9780471653974

Edition: 2008

Authors: Douglas C. Montgomery, Cheryl L. Jennings, Murat Kulahci, Douglas C. Montgomery

List price: $157.00

30 day, 100% satisfaction guarantee!

Marketplace

2 new & used from $22.50

what's this?

Rush Rewards U
Members Receive:

You have reached 400 XP and carrot coins. That is the daily max!

Description:

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.

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



Preface



Introduction to Forecasting



The Nature and Uses of Forecasts



Some Examples of Time Series



The Forecasting Process



Resources for Forecasting


Exercises



Statistics Background for Forecasting



Introduction



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



Transformations



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


Exercises



Regression Analysis and Forecasting



Introduction



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


Exercises



Exponential Smoothing Methods



Introduction



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



Forecasting



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


Exercises



Autoregressive Integrated Moving Average (ARIMA) Models



Introduction



Linear Models for Stationary Time Series



Stationarity



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


Exercises



Transfer Functions and Intervention Models



Introduction



Transfer Function Models



Transfer Function-Noise Models



Cross Correlation Function



Model Specification



Forecasting with Transfer Function-Noise Models



Intervention Analysis


Exercises



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


Exercises



Statistical Tables



Data Sets for Exercises


Bibliography


Index