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
Published Online: Mar 23 2011
Twitter: WileyCompSci Follow us on Twitter
- The future of 3D printing http://t.co/dsCLBtiaXP
- A new robot was tested with 19 people with a 100% success rate. Read to learn how it is meant to help those that are disabled....