Time Series Analysis: Forecasting and Control Author: George E. P. Box | Language: English | ISBN:
B005PS6Z2Y | Format: PDF
Time Series Analysis: Forecasting and Control Description
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.
- File Size: 17537 KB
- Print Length: 784 pages
- Publisher: Wiley; 4 edition (September 21, 2011)
- Sold by: Amazon Digital Services, Inc.
- Language: English
- ASIN: B005PS6Z2Y
- Text-to-Speech: Enabled
X-Ray:
- Lending: Enabled
- Amazon Best Sellers Rank: #459,675 Paid in Kindle Store (See Top 100 Paid in Kindle Store)
In the early 1970s I was working on practical forecasting methods to apply to the U.S. Army supply depot workloads. Exponential smoothing was the commonly used "automatic" technique (once smoothing constants have been determined) that had great advantages over the informal methods used by the Army. Then someone told me that Box-Jenkins techniques were more general and powerful. I got a copy of the first edition published in 1970 and found that I could read and understand it even though I had little statistical training. I had a bachelors degree in mathematics. I got to appreciate the book even more when I took a short course from George Box, George Tiao and David Pack based on the book. I began to grasp some of the key ideas of stationary and nonstationary time series and learned about model selection, diagnostic checking and estimation. This started my interest in becoming a statistician and gave me the practical side of time series analysis first. I later specialized in it and got a Ph.D. in statistics.
Gwilym Jenkins died many years prior to this edition and Box's colleague Greogory Reinsel took on the task of helping to revise and update it.
It retains its original flavor. It is an applied book with many practical and illustrative examples. It concentrates on the three stages of time series analysis: modeling building, selection, estimation and diagnostic checking and how to iterate the process toward a good solution. The ARIMA time series models are what are considered. The theory of stationary and nonstationary time series is introduced to motivate interpretation of autocorrelation and partial autocorrelation in the model identification phase. Operator notation is introduced and used throughout the book to simplify equations.
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