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Introduction to Time Series and Forecasting [With CDROM] (Lecture Notes in Statistics)

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Introduction to Time Series and Forecasting [With CDROM] (Lecture Notes in Statistics) Cover

ISBN13: 9780387953519
ISBN10: 0387953515
Condition:
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Synopses & Reviews

Publisher Comments:

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.

Synopsis:

This book is aimed at the reader who wishes to gain a working knowldege of time series and forecasting methods as applied in economics, engineering, and the natural and social sciences.

Synopsis:

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. This second edition contains detailed instructions on the use of the new totally windows-based computer package ITSM2000. Expanded treatments are also given of several topics treated only briefly in the first edition. These include regression with time series errors, which plays an important role in forecasting and inference, and ARCH and GARCH models, which are widely used for the modeling of financial time series. These models can be fitted using the new version of ITSM. 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 the Burg and Hannan-Rissanen algorithms, unit roots, the EM algorithm, structural models, 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.

Synopsis:

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.

Table of Contents

Introduction.- Stationary Processes.- ARMA Models.- Spectral Analysis.- Modelling and Forecasting with ARMA Processes.- Nonstationary and Seasonal Time Series Models.- Multivariate Time Series.- State-Space Models.- Forecasting Techniques.- Further Topics.- Appendix A: Random Variables and Probability Distributions.- Appendix B: Statistical Complements.- Appendix C: Mean Square Convergence.- Appendix D: An ITSM Tutorial.- References.- Index.

What Our Readers Are Saying

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Average customer rating based on 2 comments:

tvamshi, March 23, 2008 (view all comments by tvamshi)
the time series graph given is question to readers that it's analysis (nature) and prediction .
Answer to this title itself ' Introduction to time series and Forecasting'
Was this comment helpful? | Yes | No
(0 of 1 readers found this comment helpful)
rayflo90, June 28, 2007 (view all comments by rayflo90)
the title reviles that beginners can get to understand what time series is all about. and teach them all basic things about time series
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Product Details

ISBN:
9780387953519
Author:
Brockwell, Peter J.
Author:
Rockwell, P. J.
Author:
J
Author:
Brockwell, Peter
Author:
Davis, Richard A.
Publisher:
Springer
Location:
New York, NY
Subject:
Statistics
Subject:
Econometrics
Subject:
Probability
Subject:
Forecasting
Subject:
Time-series analysis
Subject:
Probability & Statistics - General
Subject:
ITSM
Subject:
ITSM2000
Subject:
Time Series
Subject:
Times series
Subject:
Statistical Theory and Methods
Subject:
Statistics for Business/Economics/Mathematical Finance/Insurance
Subject:
Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences
Subject:
Mathematics | Probability and Statistics
Subject:
Statistics for Engineering, Physics, Computer Science, Chemis
Subject:
try and Earth Sciences
Subject:
B
Subject:
mathematics and statistics
Subject:
Mathematical statistics
Subject:
Economics_xStatistics
Copyright:
Edition Number:
2
Edition Description:
8th Printing.
Series:
Springer Texts in Statistics
Series Volume:
048-01
Publication Date:
March 2003
Binding:
HARDCOVER
Language:
English
Illustrations:
Y
Pages:
451
Dimensions:
24 cm.+

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Introduction to Time Series and Forecasting [With CDROM] (Lecture Notes in Statistics) New Hardcover
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Product details 451 pages Springer Us - English 9780387953519 Reviews:
"Synopsis" by , This book is aimed at the reader who wishes to gain a working knowldege of time series and forecasting methods as applied in economics, engineering, and the natural and social sciences.
"Synopsis" by , 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. This second edition contains detailed instructions on the use of the new totally windows-based computer package ITSM2000. Expanded treatments are also given of several topics treated only briefly in the first edition. These include regression with time series errors, which plays an important role in forecasting and inference, and ARCH and GARCH models, which are widely used for the modeling of financial time series. These models can be fitted using the new version of ITSM. 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 the Burg and Hannan-Rissanen algorithms, unit roots, the EM algorithm, structural models, 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.
"Synopsis" by , 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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