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Autoregressive processes
Author(s) -
Brockwell P. J.
Publication year - 2011
Publication title -
wiley interdisciplinary reviews: computational statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.693
H-Index - 38
eISSN - 1939-0068
pISSN - 1939-5108
DOI - 10.1002/wics.163
Subject(s) - autoregressive model , autoregressive–moving average model , computer science , star model , series (stratigraphy) , time series , autoregressive integrated moving average , stochastic process , algorithm , econometrics , mathematics , statistics , machine learning , paleontology , biology
In this article, the definition, properties, and applications of linear autoregressive processes (or autoregressions) are reviewed. These form an important subset of the class of autoregressive moving‐average (ARMA) processes which are widely used as stationary models for time series data. Particular attention is paid to the problem of selecting and estimating appropriate autoregressions to describe empirically observed time series. WIREs Comp Stat 2011 3 316–331 DOI: 10.1002/wics.163 This article is categorized under: Applications of Computational Statistics > Computational Mathematics Applications of Computational Statistics > Signal and Image Processing and Coding Data: Types and Structure > Time Series, Stochastic Processes, and Functional Data