Fuzzy Time Series Inference for Stationary Linear Processes: Features and Algorithms With Simulation

Authors

Khaled M. Alqahtani, Department of Mathematics, College of Science and Humanities in Al-Kharj, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi ArabiaFollow
Mohammed H. El-Menshawy, Department of Mathematics, Faculty of Science, Al-Azhar University, Nasr city 11884, Cairo, EgyptFollow
Mohamed S. Eliwa, Department of Statistics and Operation Research, College of Science, Qassim University, Buraydah 51482, Saudi Arabia\\ Department of Mathematics, International Telematic University Uninettuno, I-00186 Rome, Italy\\ Department of Mathematics, Faculty of Science, Mansoura University, Mansoura 35516, EgyptFollow
Mahmoud El-Morshedy, Department of Mathematics, College of Science and Humanities in Al-Kharj, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia\\ Department of Statistics and Computer Science, Faculty of Science, Mansoura University, Mansoura 35516, EgyptFollow
Rashad M. EL-Sagheer, Department of Mathematics, Faculty of Science, Al-Azhar University, Nasr city 11884, Cairo, Egypt\\ High Institute of Computer and Management Information System, First Statement, New Cairo 11865, Cairo, EgyptFollow

Author Country (or Countries)

Saudi Arabia

Abstract

The primary objective of this article is to estimate the unknown parameters of stationary linear processes based on a fuzzy time series approach to observations that follow AR (1) processes. Predicted observations are obtained using fuzzy time series. Both actual and forecasted observations are utilized to study various classic method’s estimators for the autoregressive parameter. The comparisons between actual and forecasted observations in all estimating processes are discussed based on the mean squared errors. Furthermore, to investigate the extent to which fuzzy time series can enhance estimates produced by traditional estimating techniques. Based on these comparisons, it is possible to explore how fuzzy time series contribute to the improvement of classical methods’ estimations.

Digital Object Identifier (DOI)

http://dx.doi.org/10.18576/amis/170302

Recommended Citation

M. Alqahtani, Khaled; H. El-Menshawy, Mohammed; S. Eliwa, Mohamed; El-Morshedy, Mahmoud; and M. EL-Sagheer, Rashad (2023) "Fuzzy Time Series Inference for Stationary Linear Processes: Features and Algorithms With Simulation," Applied Mathematics & Information Sciences: Vol. 17: Iss. 3, Article 2.
DOI: http://dx.doi.org/10.18576/amis/170302
Available at: https://digitalcommons.aaru.edu.jo/amis/vol17/iss3/2