Autoregressive Models of the Random fields—A Survey | ||
| IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING | ||
| Article 1, Volume 20, Issue 2, April 2020, Pages 1-9 PDF (620.61 K) | ||
| Authors | ||
| Hussein A. Abdulkadhim1; Mayyadah S. Ibrahim2; Ali N. Albu-Rghaif3 | ||
| 1Dept. of Communication Eng., College of Engineering, University of Diyala, Diyala, Iraq. | ||
| 2Dept. of Power Eng., College of Engineering, University of Diyala, Diyala, Iraq | ||
| 3Dept. of Computer Eng., College of Engineering, University of Diyala, Diyala, Iraq. | ||
| Abstract | ||
| Autoregressive (AR) random fields are widely use to describe changes in the status of real-physical objects and implemented for analyzing linear & non-linear models. AR models are Markov processes with a higher order dependence for one-dimensional time series. Actually, various estimation methods were used in order to evaluate the autoregression parameters. Although in many applications background knowledge can often shed light on the search for a suitable model, but other applications lack this knowledge and often require the type of trial errors to choose a model. This article presents a brief survey of the literatures related to the linear and non-linear autoregression models, including several extensions of the main mode models and the models developed. The use of autoregression to describe such system requires that they be of sufficiently high orders which leads to increase the computational costs | ||
| Keywords | ||
| Autoregressive process; Exponential autoregressive models; Threshold autoregressive models; Vector autoregressive models | ||
|
Statistics Article View: 1,747 PDF Download: 141 |
||