Table of Content

Open Access iconOpen Access

ARTICLE

crossmark

The m-delay Autoregressive Model with Application

Manlika Ratchagit1, Benchawan Wiwatanapataphee1, Nikolai Dokuchaev1, *

1 School of Electrical Engineering, Computing and Mathematical Sciences, Curtin University, Perth, Australia.

* Corresponding Author: Nikolai Dokuchaev. Email: email.

Computer Modeling in Engineering & Sciences 2020, 122(2), 487-504. https://doi.org/10.32604/cmes.2020.08865

Abstract

The classical autoregressive (AR) model has been widely applied to predict future data using m past observations over five decades. As the classical AR model required m unknown parameters, this paper implements the AR model by reducing m parameters to two parameters to obtain a new model with an optimal delay called as the m-delay AR model. We derive the m-delay AR formula for approximating two unknown parameters based on the least squares method and develop an algorithm to determine optimal delay based on a brute-force technique. The performance of the m-delay AR model was tested by comparing with the classical AR model. The results, obtained from Monte Carlo simulation using the monthly mean minimum temperature in Perth Western Australia from the Bureau of Meteorology, are no significant difference compared to those obtained from the classical AR model. This confirms that the m-delay AR model is an effective model for time series analysis.

Keywords


Cite This Article

APA Style
Ratchagit, M., Wiwatanapataphee, B., Dokuchaev, N. (2020). The <i>m</i>-delay autoregressive model with application. Computer Modeling in Engineering & Sciences, 122(2), 487-504. https://doi.org/10.32604/cmes.2020.08865
Vancouver Style
Ratchagit M, Wiwatanapataphee B, Dokuchaev N. The <i>m</i>-delay autoregressive model with application. Comput Model Eng Sci. 2020;122(2):487-504 https://doi.org/10.32604/cmes.2020.08865
IEEE Style
M. Ratchagit, B. Wiwatanapataphee, and N. Dokuchaev "The <i>m</i>-delay Autoregressive Model with Application," Comput. Model. Eng. Sci., vol. 122, no. 2, pp. 487-504. 2020. https://doi.org/10.32604/cmes.2020.08865



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
  • 3571

    View

  • 1893

    Download

  • 0

    Like

Related articles

Share Link