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Time Series Analysis: Forecasting and Control
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Publisher:John Wiley & Sons
Publication Date:2008-06-01
Number of Pages:746 Pages
Book Type:hardcover
Weight:1192 gram
Books Dimensions:241 x 165 x 43 mm
Format of ebooks: PDF(Acrobat Reader) or Word version doc Document
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Time Series Analysis: Forecasting and Control

- 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 toolsNew coverage of forecasting in the design of feedback and feedforward control schemesA 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 processesCoverage of structural component models for the modeling, forecasting, and seasonal adjustment of Time seriesA review of the maximum likelihood estimation for ARMA models with missing valuesNumerous 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.

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