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Time Series : Theory and Methods.

By: Contributor(s): Material type: TextTextSeries: Springer Series in Statistics SeriesPublisher: New York, NY : Springer, 2009Copyright date: ©1991Edition: 2nd edDescription: 1 online resource (591 pages)Content type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9781441903204
Subject(s): Genre/Form: Additional physical formats: Print version:: Time Series: Theory and MethodsLOC classification:
  • QA276-280
Online resources:
Contents:
Intro -- Preface to the Second Edition -- Preface to the First Edition -- Contents -- Stationary Time Series -- Hilbert Spaces -- Stationary ARMA Processes -- The Spectral Representation of a Stationary Process -- Prediction of Stationary Processes -- Asymptotic Theory -- Estimation of the Mean and the Autocovariance Function -- Estimation for ARMA Models -- Model Building and Forecasting with ARIMA Processes -- Inference for the Spectrum of a Stationary Process -- Multivariate Time Series -- State-Space Models and the Kalman Recursions -- Further Topics -- Data Sets -- Bibliography -- Index.
Summary: Here is a systematic account of linear time series models and their application to the modeling and prediction of data collected sequentially in time. It details techniques for handling data and offers a thorough understanding of their mathematical basis.
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Intro -- Preface to the Second Edition -- Preface to the First Edition -- Contents -- Stationary Time Series -- Hilbert Spaces -- Stationary ARMA Processes -- The Spectral Representation of a Stationary Process -- Prediction of Stationary Processes -- Asymptotic Theory -- Estimation of the Mean and the Autocovariance Function -- Estimation for ARMA Models -- Model Building and Forecasting with ARIMA Processes -- Inference for the Spectrum of a Stationary Process -- Multivariate Time Series -- State-Space Models and the Kalman Recursions -- Further Topics -- Data Sets -- Bibliography -- Index.

Here is a systematic account of linear time series models and their application to the modeling and prediction of data collected sequentially in time. It details techniques for handling data and offers a thorough understanding of their mathematical basis.

Description based on publisher supplied metadata and other sources.

Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.

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