rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Introduction to Time Series and Forecasting
  name:
    name part: Brockwell, Peter J.
    role:
      role term: editor.
  name:
    name part: Davis, Richard A.
    role:
      role term: editor.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: xxu
    date issued: 2002
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XIV, 437 p. online resource.
  abstract: 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.
  table of contents: Stationary Processes -- ARMA Models -- Spectral Analysis -- Modeling and Forecasting with ARMA Processes -- Nonstationary and Seasonal Time Series Models -- Multivariate Time Series -- State-Space Models -- Forecasting Techniques -- Further Topics -- Erratum.
  note: edited by Peter J. Brockwell, Richard A. Davis.
  subject:
    topic: Mathematics
  subject:
    topic: Computer software
  subject:
    topic: Probabilities
  subject:
    topic: Statistics
  subject:
    topic: Econometrics
  subject:
    topic: Mathematics
  subject:
    topic: Mathematical Software
  subject:
    topic: Probability Theory and Stochastic Processes
  subject:
    topic: Statistical Theory and Methods
  subject:
    topic: Statistics for Business/Economics/Mathematical Finance/Insurance
  subject:
    topic: Econometrics
  subject:
    topic: Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences
  classification: QA76.75-76.765
  classification: 004
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Springer Texts in Statistics
  identifier: 9780387216577
  identifier: http://dx.doi.org/10.1007/b97391
  record info:
    record creation date: 100301
    record change date: 20151204185844.0
    record identifier: 978-0-387-21657-7