Introduction to Time Series and Forecasting (Springer Texts in Statistics)

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9780387953519: Introduction to Time Series and Forecasting (Springer Texts in Statistics)

This is an introduction to time series that emphasizes methods and analysis of data sets. The logic and tools of model-building for stationary and non-stationary time series are developed and numerous exercises, many of which make use of the included computer package, provide the reader with ample opportunity to develop skills. Statisticians and students will learn the latest methods in time series and forecasting, along with modern computational models and algorithms.

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About the Author:

Dr. Richard A. Davis Jr. was a professor of geology at the University of South Florida until 2005. He is now an emeritus distinguished university professor at USF and visiting professor/research associate at Harte Research Institute, Texas A&M University in Corpus Christi, Texas. His area of specialization is coastal geology with emphasis on beaches, barrier islands, and tidal inlets. He has written/edited 20 books and about 150 journal articles on topics of oceanography, coastal geology, stratigraphy, and sedimentology. He has been a visiting professor in Australia, New Zealand, the Netherlands, Denmark, Spain, and Germany. And he speaks and teaches workshops at universities all over the world. He holds the Shepard Medal in Marine Geology.

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From the reviews:
"The emphasis is on hands-on experience and the friendly software that accompanies the book serves the purpose admirably. ...
The authors should be congratulated for making the subject accessible and fun to learn. The book is a pleasure to read and highly recommended. I regard it as the best introductory text in town." ISI Short Book Reviews

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Book Description Springer-Verlag New York Inc., United States, 2010. Mixed media product. Book Condition: New. 2nd ed. 2002. Corr. 9th printing 2010. Language: English . Brand New Book. 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 nonstationary 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 algorithms, 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 nonlinear, 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, 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 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. Bookseller Inventory # KNV9780387953519

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Book Description Springer-Verlag New York Inc., United States, 2010. Mixed media product. Book Condition: New. 2nd ed. 2002. Corr. 9th printing 2010. Language: English . Brand New Book. 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 nonstationary 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 algorithms, 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 nonlinear, 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, 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 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. Bookseller Inventory # KNV9780387953519

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INTRODUCTION TO TIME SERIES AND FORECASTING -
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Book Description Berlin Springer Okt 2001, 2001. Buch. Book Condition: Neu. Neuware - This is an introduction to time series that emphasizes methods and analysis of data sets. The logic and tools of model-building for stationary and non-stationary time series are developed and numerous exercises, many of which make use of the included computer package, provide the reader with ample opportunity to develop skills. Statisticians and students will learn the latest methods in time series and forecasting, along with modern computational models and algorithms. 434 pp. Englisch. Bookseller Inventory # 9780387953519

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