Applied time series analysis
 Responsibility
 Wayne A. Woodward, Henry L. Gray, Alan C. Elliott.
 Language
 English.
 Imprint
 Boca Raton, FL : CRC Press, c2012.
 Physical description
 xxiii, 540 p. : ill. ; 25 cm.
 Series
 Statistics, textbooks and monographs.
Access
Available online
Math & Statistics Library
Stacks
Call number  Status 

QA280 .W66 2012  Unknown 
More options
Creators/Contributors
 Author/Creator
 Woodward, Wayne A.
 Contributor
 Gray, Henry L.
 Elliott, Alan C.
Contents/Summary
 Bibliography
 Includes bibliographical references (p. 519527) and index.
 Contents

 Stationary Time Series Time Series Stationary Time Series Autocovariance and Autocorrelation Functions for Stationary Time Series Estimation of the Mean, Autocovariance, and Autocorrelation for Stationary Time Series Power Spectrum Estimating the Power Spectrum and Spectral Density for Discrete Time Series Time Series Examples Linear Filters Introduction to Linear Filters Stationary General Linear Processes Wold Decomposition Theorem Filtering Applications ARMA Time Series Models Moving Average Processes Autoregressive Processes AutoregressiveMoving Average Processes Visualizing Autoregressive Components Seasonal ARMA(p, q)x(Ps, Qs)s Models Generating Realizations from ARMA(p, q) Processes Transformations Other Stationary Time Series Models Stationary Harmonic Models ARCH and GARCH Models Nonstationary Time Series Models Deterministic SignalPlusNoise Models ARIMA(p, d, q) and ARUMA(p, d, q) Models Multiplicative Seasonal ARUMA(p, d, q) x (Ps, Ds, Qs)s Model Random Walk Models GStationary Models for Data with TimeVarying Frequencies Forecasting Mean Square Prediction Background BoxJenkins Forecasting for ARMA(p, q) Models Properties of the Best Forecast Xto(l) piWeight Form of the Forecast Function Forecasting Based on the Difference Equation Eventual Forecast Function Probability Limits for Forecasts Forecasts Using ARUMA(p, d, q) Models Forecasts Using Multiplicative Seasonal ARUMA Models Forecasts Based on SignalplusNoise Models Parameter Estimation Introduction Preliminary Estimates Maximum Likelihood Estimation of ARMA( p, q) Parameters Backcasting and Estimating sigma2a Asymptotic Properties of Estimators Estimation Examples Using Data ARMA Spectral Estimation ARUMA Spectral Estimation Model Identification Preliminary Check for White Noise Model Identification for Stationary ARMA Models Model Identification for Nonstationary ARUMA(p, d, q) Models Model Identification Based on Pattern Recognition Model Building Residual Analysis Stationarity versus Nonstationarity SignalplusNoise versus Purely AutocorrelationDriven Models Checking Realization Characteristics Comprehensive Analysis of Time Series Data: A Summary VectorValued (Multivariate) Time Series Multivariate Time Series Basics Stationary Multivariate Time Series Multivariate (Vector) ARMA Processes Nonstationary VARMA Processes Testing for Association between Time Series StateSpace Models Proof of Kalman Recursion for Prediction and Filtering LongMemory Processes Long Memory Fractional Difference and FARMA Models Gegenbauer and GARMA Processes kFactor Gegenbauer and GARMA Models Parameter Estimation and Model Identification Forecasting Based on the kFactor GARMA Model Modeling Atmospheric CO2 Data Using LongMemory Models Wavelets Shortcomings of Traditional Spectral Analysis for TVF Data Methods That Localize the "Spectrum" in Time Wavelet Analysis Wavelet Packets Concluding Remarks on Wavelets Appendix: Mathematical Preliminaries for This Chapter GStationary Processes GeneralizedStationary Processes MStationary Processes G(lambda)Stationary Processes Linear Chirp Processes Concluding Remarks Index.
 (source: Nielsen Book Data)
 Publisher's Summary
 Virtually any random process developing chronologically can be viewed as a time series. In economics, closing prices of stocks, the cost of money, the jobless rate, and retail sales are just a few examples of many. Developed from course notes and extensively classroomtested, Applied Time Series Analysis includes examples across a variety of fields, develops theory, and provides software to address time series problems in a broad spectrum of fields. The authors organize the information in such a format that graduate students in applied science, statistics, and economics can satisfactorily navigate their way through the book while maintaining mathematical rigor. One of the unique features of Applied Time Series Analysis is the associated software, GWWINKS, designed to help students easily generate realizations from models and explore the associated model and data characteristics. The text explores many important new methodologies that have developed in time series, such as ARCH and GARCH processes, time varying frequencies (TVF), wavelets, and more. Other programs (some written in R and some requiring Splus) are available on an associated website for performing computations related to the material in the final four chapters.
(source: Nielsen Book Data)
Subjects
 Subject
 Timeseries analysis.
Bibliographic information
 Publication date
 2012
 Series
 Statistics: textbooks and monographs
 ISBN
 9781439818374 (alk. paper)
 1439818371 (alk. paper)