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Wiley Series in Probability and Statistics Ser.: Time Series Analysis : Forecasting and Control by Gwilym M. Jenkins, George E. P. Box and Gregory C. Reinsel (2008, Hardcover)
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À propos de ce produit
Product Identifiers
- PublisherWiley & Sons, Incorporated, John
- ISBN-100470272848
- ISBN-139780470272848
- eBay Product ID (ePID)63177228
Product Key Features
- Number of Pages784 Pages
- Publication NameTime Series Analysis : Forecasting and Control
- LanguageEnglish
- Publication Year2008
- SubjectProbability & Statistics / Stochastic Processes, Probability & Statistics / General, Probability & Statistics / Time Series, Applied
- TypeTextbook
- Subject AreaMathematics
- AuthorGwilym M. Jenkins, Gregory C. Reinsel, George E. P. Box
- SeriesWiley Series in Probability and Statistics Ser.
- FormatHardcover
Dimensions
- Item Height1.5 in
- Item Weight42.8 Oz
- Item Length9.4 in
- Item Width6.3 in
Additional Product Features
- Edition Number4
- Intended AudienceScholarly & Professional
- LCCN2007-044569
- Reviews'The book follows faithfully the style of the original edition. The approach is heavily motivated by real world time series, and by developing a complete approach to model building, estimation, forecasting and control.' ( Mathematical Reviews , 2009) "I think the book is very valuable and useful to graduate students in statistics, mathematics, engineering, and the like. Also, it could be of tremendous help to practioners. Even though the book is written in a clear, easy to follow narrative style with plenty of illustrations, one should nevertheless have a sufficient knowledge of graduate level mathematical statistics. By reading and understanding the book one should, in the end, feel very confident in time series and analysis." ( MAA Reviews , January 13, 2009) "I think the book is very valuable and useful to graduate students in statistics, mathematics, engineering, and the like. Also, it could be of tremendous help to practioners. Even though the book is written in a clear, easy to follow narrative style with plenty of illustrations, one should nevertheless have a sufficient knowledge of graduate level mathematical statistics. By reading and understanding the book one should, in the end, feel very confident in time series and analysis." ( MAA Reviews, January 2009), "I think the book is very valuable and useful to graduate students in statistics, mathematics, engineering, and the like. Also, it could be of tremendous help to practioners. Even though the book is written in a clear, easy to follow narrative style with plenty of illustrations, one should nevertheless have a sufficient knowledge of graduate level mathematical statistics. By reading and understanding the book one should, in the end, feel very confident in time series and analysis." ( MAA Reviews, January 2009)
- Dewey Edition23
- Series Volume Number734
- IllustratedYes
- Dewey Decimal519.5/5
- Table Of ContentPreface to the Fourth Edition.Preface to the Third Edition.1. Introduction.1.1 Five Important Practical Problems.1.2 Stochastic and Deterministic Dynamic Mathematical Models.Part One: Stochastic Models and Their Forecasting.2. Autocorrelation Function and Spectrum of Stationary Processes.2.1 Autocorelation Properties of Stationary Models.2.2 Spectral Properties of Stationary Models.3. Linear Stationary Models.3.1 General Linear Process.3.2 Autoregressive Processes.3.3 Moving Average Processes.3.4 Mixed Autoregressive-Moving Average Processes4. Linear Nonstationary Models.4.1 Autoregressive Integrated Moving Average Processes.4.2 Three Explicit Forms for the Autoregressive Integrated Moving Average Model.4.3 Integrated Moving Average Processes.5. Forecasting.5.1 Minimum Mean Square Error Forecasts and Their Properties.5.2 Calculating and Updating Forecasts.5.3 Forecast Function and Forecast Wrights.5.4 Example of Forecast Functions and Their Updating.5.5 Use of State-Space Model Formulation for Exact Forecasting.5.6 Summary.Part Two: Stochastic Model Building.6. Model Identification.6.1 Objective of Identification.6.2 Indetification Techniques.6.3 Initial Estimates for the Parameters.6.4 Model Multiplicity.7. Model Estimation.7.1 Study of the Likelihood and Sum-of-Squares Functions.7.2 Nonlinear Estimation.7.3 Some Estimation Results for Specific Models.7.4 Likelihood Function Based on the State-Space Model.7.5 Unit Roots in Arima Models.7.6 Estimation Using Bayes's Theorem.8. Model Diagnostic Checking.8.1 Checking the Stochastic Model.8.2 Diagnostic Checks Applied to Residuals.8.3 Use of Residuals to Modify the Model.9. Seasonal Models.9.1 Parsimonious Models for Seasonal Time Series.9.2 Representation of the Airline Data by a Multiplicative.9.3 Some Aspects of More General Seasonal ARIMA Models.9.4 Structural Component Models and Deterministic Seasonal Components.9.5 Regression Models with Time Error Terms.10. Nonlinear and Long Memory Models.10.1 Autoregressive Conditional Heteroscedastic (ARCH) Models.10.2 Nonlinear Time Series Models.10.3 Long memory Time Series Processes.Part Three: Transfer Function and Multivariate Model Building.11. Transfer Function Models.11.1 Linear Transfer Function Models.11.2 Discrete Dynamic Models Represented by Difference Equations.11.3 Relation Between Discrete and Continuous Models.12. Identification, Fitting, and Checking of Transfer Function Models.12.1 Cross-Correlation Function.12.2 Identification of Transfer Function Models.12.3 Fitting and Checking Transfer Function Models.12.4 Some Examples of Fitting and Checking Transfer Function Models.12.5 Forecasting with Transfer Function Models Using Leading Indicators.12.6 Some Aspects of the Design of Experiments to Estimate Transfer Functions.13. Intervention Analysis Models and Outlier Detection.13.1 Intervention Analysis Methods.13.2 Outlier Analysis for Time Series.13.3 Estimation for ARMA Models with Missing Values.14. Multivariate Time Series Analysis.14.1 Stationary Multivariate Time Series.14.2 Linear Model Representations for Stationary Multivariate Processes.14.3 Nonstationary Vector Autoregressive-Moving Average Models.14.4 Forecasting for Vector Autoregressive-Moving Average Processes.14.5 State-Space Form of the Vector ARMA Models.
- Synopsis- Time Series Analysis is the seminal book of choice for a clear and practical understanding of time series as it relates to the applied areas of forecasting and quality control. - This new edition is a revision of a classic and authoritative book that has been the model for most books on the topic written since 1970., A modernized new edition of one of the most trusted books on time series analysis. Since publication of the first edition in 1970, Time Series Analysis has served as one of the most influential and prominent works on the subject. This new edition maintains its balanced presentation of the tools for modeling and analyzing time series and also introduces the latest developments that have occurred n the field over the past decade through applications from areas such as business, finance, and engineering. The Fourth Edition provides a clearly written exploration of the key methods for building, classifying, testing, and analyzing stochastic models for time series as well as their use in five important areas of application: forecasting; determining the transfer function of a system; modeling the effects of intervention events; developing multivariate dynamic models; and designing simple control schemes. Along with these classical uses, modern topics are introduced through the book's new features, which include: A new chapter on multivariate time series analysis, including a discussion of the challenge that arise with their modeling and an outline of the necessary analytical tools New coverage of forecasting in the design of feedback and feedforward control schemes A new chapter on nonlinear and long memory models, which explores additional models for application such as heteroscedastic time series, nonlinear time series models, and models for long memory processes Coverage of structural component models for the modeling, forecasting, and seasonal adjustment of time series A review of the maximum likelihood estimation for ARMA models with missing values Numerous illustrations and detailed appendices supplement the book, while extensive references and discussion questions at the end of each chapter facilitate an in-depth understanding of both time-tested and modern concepts. With its focus on practical, rather than heavily mathematical, techniques, Time Series Analysis , Fourth Edition is the upper-undergraduate and graduate levels. this book is also an invaluable reference for applied statisticians, engineers, and financial analysts., A modernized new edition of one of the most trusted books on time series analysis. Since publication of the first edition in 1970, Time Series Analysis has served as one of the most influential and prominent works on the subject. This new edition maintains its balanced presentation of the tools for modeling and analyzing time series and also introduces the latest developments that have occurred n the field over the past decade through applications from areas such as business, finance, and engineering. The Fourth Edition provides a clearly written exploration of the key methods for building, classifying, testing, and analyzing stochastic models for time series as well as their use in five important areas of application: forecasting; determining the transfer function of a system; modeling the effects of intervention events; developing multivariate dynamic models; and designing simple control schemes. Along with these classical uses, modern topics are introduced through the book's new features, which include: A new chapter on multivariate time series analysis, including a discussion of the challenge that arise with their modeling and an outline of the necessary analytical tools New coverage of forecasting in the design of feedback and feedforward control schemes A new chapter on nonlinear and long memory models, which explores additional models for application such as heteroscedastic time series, nonlinear time series models, and models for long memory processes Coverage of structural component models for the modeling, forecasting, and seasonal adjustment of time series A review of the maximum likelihood estimation for ARMA models with missing values Numerous illustrations and detailed appendices supplement the book,while extensive references and discussion questions at the end of each chapter facilitate an in-depth understanding of both time-tested and modern concepts. With its focus on practical, rather than heavily mathematical, techniques, Time Series Analysis , Fourth Edition is the upper-undergraduate and graduate levels. this book is also an invaluable reference for applied statisticians, engineers, and financial analysts., A modernized new edition of one of the most trusted books on time series analysis. Since publication of the first edition in 1970, Time Series Analysis has served as one of the most influential and prominent works on the subject. This new edition maintains its balanced presentation of the tools for modeling and analyzing time series and also introduces the latest developments that have occurred n the field over the past decade through applications from areas such as business, finance, and engineering. The Fourth Edition provides a clearly written exploration of the key methods for building, classifying, testing, and analyzing stochastic models for time series as well as their use in five important areas of application: forecasting; determining the transfer function of a system; modeling the effects of intervention events; developing multivariate dynamic models; and designing simple control schemes. Along with these classical uses, modern topics are introduced through the book's new features, which include: A new chapter on multivariate time series analysis, including a discussion of the challenge that arise with their modeling and an outline of the necessary analytical tools New coverage of forecasting in the design of feedback and feedforward control schemes A new chapter on nonlinear and long memory models, which explores additional models for application such as heteroscedastic time series, nonlinear time series models, and models for long memory processes Coverage of structural component models for the modeling, forecasting, and seasonal adjustment of time series A review of the maximum likelihood estimation for ARMA models with missing values Numerous illustrations and detailed appendices supplement the book,while extensive references and discussion questions at the end of each chapter facilitate an in-depth understanding of both time-tested and modern concepts. With its focus on practical, rather than heavily mathematical, techniques, time Series Analysis, Fourth Edition is the upper-undergraduate and graduate levels. this book is also an invaluable reference for applied statisticians, engineers, and financial analysts.
- LC Classification NumberQA280.B67 2008