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by Peter J. Brockwell

Author:	Peter J. Brockwell
Subcategory:	Mathematics
Language:	English
Publisher:	Springer Verlag; Har/Dskt edition (July 1, 1996)
Pages:	420 pages
Category:	Math and Science
Rating:	4.2
Other formats:	lrf rtf lrf lrf

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Time Series Analysis and Its Applications: With R Examples (Springer Texts in Statistics) by Robert H. Shumway Paperback .

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Introduction to time series and forecasting, Peter J. Brockwell and Richard A. Davis. 2nd ed. p. cm. - (Springer texts in statistics). This book is aimed at the reader who wishes to gain a working knowledge of time series and forecasting methods as applied in economics, engineering and the natural and social sciences. Unlike our earlier book, Time Series: Theory and Methods, re-ferred to in the text as TSTM, this one requires only a knowledge of basic calculus, matrix algebra and elementary statistics at the level (for example) of Mendenhall, Wackerly and Scheaffer (1990). It is intended for upper-level undergraduate students and beginning graduate students.

This book is aimed at the reader who wishes to gain a working knowledge of time series and forecasting methods as. .

This book is aimed at the reader who wishes to gain a working knowledge of time series and forecasting methods as applied to economics, engineering and the natural and social sciences. It assumes knowledge only of basic calculus, matrix algebra and elementary statistics. This is a very well-written textbook aimed at a wide audience of readers interested in time series methodologies and their applications to various fields.

Authors: Brockwell, Peter . Davis, Richard . The time series package included in the back of the book is a slightly modified version of the package ITSM, published separately as ITSM for Windows, by Springer-Verlag, 1994

The time series package included in the back of the book is a slightly modified version of the package ITSM, published separately as ITSM for Windows, by Springer-Verlag, 1994. It does not handle such large data sets as ITSM for Windows, but like the latter, runs on IBM-PC compatible computers under either DOS or Windows (version . or later). The programs are all menu-driven so that the reader can immediately apply the techniques in the book to time series data, with a minimal investment of time in the computational and algorithmic aspects of the analysis.

This book is aimed at the reader who wishes to gain a working knowledge of time series and forecasting methods as applied in economics, engineering, and the natural and social sciences. The book assumes knowledge only of basic calculus, matrix algebra and elementary statistics.

Springer Texts in Statistics. Alfred: Elements of Statistics for the Life and Social Sciences Berger: An Introduction to Probability and Stochastic Processes Bilodeau and Brenner: Theory of Multivariate Statistics Blom: Probability and Statistics: Theory and Applications Brockwell and Davis: Introduction to Times Series and Forecasting, Second. It is a recently developed area in statistics and blends with parallel developments in computer science and, in particular, machine learning. The eld encompasses many methods such as the lasso and sparse regression, classication and regression trees, and boosting and support vector machines. 1. 2 1. Introduction.

Peter J. Brockwell Department of Statistics Colorado State University Fort Collins, CO 80523 USA . Brockwell Department of Statistics Colorado State University Fort Collins, CO 80523 USA pjbrock. - (Springer texts in statistics) Includes bibliographical references and index. ISBN 0-387-95351-5 (alk. paper) 1. Time-series analysis. I. Davis, Richard A. II.

Introduction to Time Series Analysis and Forecasting. Douglas C. Montgomery. Introduction to. TiMe SerieS. 19 MB·18,059 Downloads. Next Generation Earth System Prediction: Strategies for Subseasonal to Seasonal Forecasts. 47 MB·2,943 Downloads·New! from weather, climate, and other natural phenomena. For several decades, forecasts with lead times. Introduction to Insurance Mathematics: Technical and Financial Features of Risk Transfers. 19 MB·9,707 Downloads·New!

ooks/Stat/(Springer Texts in Statistics) Peter J. Brockwell, Richard A. Davis (auth. -Introduction to Time Series and International Publishing (2016). Fetching contributor. annot retrieve contributors at this time. 6 MB. Download History.

This book is aimed at those who wish to gain a working knowledge of time series and forecasting methods as applied in economics, engineering,and the natural and social sciences. Some of the key mathematical results are stated without proof in order to make the underlying theory accessible to a wider audience. The book assumes a knowledge only of basic calculus, matrix algebra, and elementary statistics. The emphasis is on methods and the analysis of data sets. The logic and tools of model-building for stationary and non-stationary time series are developed in detail and numerous exercises, many of which make use of the included computer package, provide the reader with ample opportunity to develop skills in this area. The core of the book covers stationary processes, ARMA and ARIMA processes, multivariate time series and state-space models, with an optional chapter on spectral analysis. Additional topics include harmonic regression, the Burg and Hannan-Rissanen al! gorithms, unit roots, regression with ARMA errors, structural models, the EM algorithm, generalized state-space models with applications to time series of count data, exponential smoothing, the Holt-Winters and ARAR forecasting algorithms, transfer function models and intervention analysis. Brief introductions are also given to cointegration and to non-linear continuous-time and long -memory models. The time series package included in the back of the book is a slightly modified version of the package ITSM, published separately as ITSM for Windows, Springer-Verlag, 1994. It does not handle such large data sets as ITSM for Windows, but like the latter, runs on IBM-PC compatible computers under either DOS or Windows (Version 3.1 or later). The programs are all menu-driven so that the reader can immediately apply the techniques in the book to time series data, with a minimal investment of time in the computational and algorithmic aspects of the analysis.

Comments:

Jeronashe

For an introduction, too few words, only formulas. No hints, no intuitive explanations. This is formula and that is formula. Lets prove that this formula is that formula. It is not that math is complicated, but it is math for the sake of math. I believe this book could be written by artificial intelligence for robots.

Arashitilar

Not very good, IMO.

The software used has no application in the modern world--why a professor would chose a book that teaches zero skills in the real world is beyond me.

The math is very simple--yet somehow, this book makes it 10x harder than it should be. I consistently find myself referring to other texts to avoid having to deal with this book. Its like Common Core for time series.

If you're a professor and you give this book to your students, you're not doing them any favors.

Conjukus

A decent basic introduction covering a lot of topics. It's much more accessible for learning the subject for the first time then many other books which pile on the mathematical notation and obscure the actual meaning of things. The accompanying CD is very nice, although it gets annoying very fast that you're restricted to very small dataset sizes---but it does help in learning. The only two things that are somewhat of a problem with this book are 1) many times, rather than clearly stating "here's the algorithm you need to implement", you are referred to 3 or 4 other sections of the book for pieces of the algorithm, often without a clear explanation of exactly how that earlier section is supposed to be worked into the current desired algorithm and 2) there aren't a lot of practical insights as to how to actually initialize many of the algorithms (everything is great if you already know all the parameters in advance but starting from scratch with just raw data isn't dealt with I think as fully as would be useful). All in all, though, the book is helpful and, as I said, very good for learning the essential concepts for the first time.

Iraraeal

The title of this textbook is "Introduction to Time Series and Forecasting". The materials presented in this book is of the appropriate level. My main problem with the book is how "concise" it is. In buying a textbook, I hope to buy a reference book that would explain things well. In this book, materials are presented without much details.

Now, I don't expect the textbook to spoon feed you every single minutiae. However, when theorems are presented with only a 3-4 line proof, omitting all the details, and that the in-chapter examples read like a solution manual, it leaves much to be desired for the self studier.
More often than not, the book states things and the only explanation given being "it can be easily shown that..."

When I took this course for my math master, I had a good professor that was able to fill in the omitted details of this book. In that sense, this was a good companion for the course. However, if you are looking to study by yourself, then you're better off using books with more details and backgrounds.

Dandr

I'm sure it's a great book for people with advanced mathematics backgrounds, but it's simply NOT an introductory-level text, and it's unsuitable for engineers and other scientists. Definitely not an undergraduate-level text (although I should mention I'm trying to use it for my graduate-level coursework.) I wish I could rate the actual content of the book, but it's too incomprehensible for me to give an informed appraisal. Suffice it to say that the book's title misrepresents the content. THIS IS NOT AN INTRODUCTION.

Apart from that, the exercise problems have no solutions given, and Springer (the book's publisher) refuses to let me (a graduate student) have access to the solutions manual. I'd have to be teaching a class using the book, for which I'd need to understand the material, for which a solutions manual would help - you get the idea.

Waiso

This is a good introductory book. Definitely written for an undergraduate course. The software included and used in the text (ITSM) is very user friendly. But if you're looking for a rigorous theoretical book on time series, this is not the book for you. If you're looking for a good introductory text with lots of examples, this would be a good book for you.

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