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Peña, Daniel

A Course in Time Series Analysis

Peña, Daniel - A Course in Time Series Analysis, ebook

133,10€

Ebook, PDF with Adobe DRM
ISBN: 9781118031223
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New statistical methods and future directions of research in time series

A Course in Time Series Analysis demonstrates how to build time series models for univariate and multivariate time series data. It brings together material previously available only in the professional literature and presents a unified view of the most advanced procedures available for time series model building. The authors begin with basic concepts in univariate time series, providing an up-to-date presentation of ARIMA models, including the Kalman filter, outlier analysis, automatic methods for building ARIMA models, and signal extraction. They then move on to advanced topics, focusing on heteroscedastic models, nonlinear time series models, Bayesian time series analysis, nonparametric time series analysis, and neural networks. Multivariate time series coverage includes presentations on vector ARMA models, cointegration, and multivariate linear systems. Special features include:

  • Contributions from eleven of the world???s leading figures in time series
  • Shared balance between theory and application
  • Exercise series sets
  • Many real data examples
  • Consistent style and clear, common notation in all contributions
  • 60 helpful graphs and tables

    Requiring no previous knowledge of the subject, A Course in Time Series Analysis is an important reference and a highly useful resource for researchers and practitioners in statistics, economics, business, engineering, and environmental analysis.

    An Instructor's Manual presenting detailed solutions to all the problems in the book is available upon request from the Wiley editorial department.

    Keywords: Time Series

  • Author(s)
     
     
    Publisher
    John Wiley and Sons, Inc.
    Publication year
    2001
    Language
    en
    Edition
    1
    Series
    Wiley Series in Probability and Statistics
    Page amount
    496 pages
    Category
    Natural Sciences
    Format
    Ebook
    eISBN (PDF)
    9781118031223
    Printed ISBN
    9780471361640

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