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Overview of Identification Methods of Autoregressive Model in Presence of Additive Noise

Author

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  • Dmitriy Ivanov

    (Department of Information Systems Security, Samara National Research University, 443086 Samara, Russia
    Department of Mechatronics, Samara State University of Transport, 443066 Samara, Russia)

  • Zaineb Yakoub

    (Department of Electrical Engineering, National Engineering School of Gabes, University of Gabes, Gabes 6029, Tunisia)

Abstract

This paper presents an overview of the main methods used to identify autoregressive models with additive noises. The classification of identification methods is given. For each group of methods, advantages and disadvantages are indicated. The article presents the simulation results of a large number of the described methods and gives recommendations on choosing the best methods.

Suggested Citation

  • Dmitriy Ivanov & Zaineb Yakoub, 2023. "Overview of Identification Methods of Autoregressive Model in Presence of Additive Noise," Mathematics, MDPI, vol. 11(3), pages 1-21, January.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:3:p:607-:d:1046884
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    References listed on IDEAS

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    1. Poskitt, D.S., 1994. "A Note on Autoregressive Modeling," Econometric Theory, Cambridge University Press, vol. 10(5), pages 884-899, December.
    2. Wei Xing Zheng, 2003. "Study of a least-squares-based algorithm for autoregressive signals subject to white noise," Mathematical Problems in Engineering, Hindawi, vol. 2003, pages 1-9, January.
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