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294123

Fuzzy Time Series Forecasting: Chen, Markov Chain and Cheng Models

Article

Last updated: 24 Dec 2024

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Abstract

This paper studies and reviews several procedures for Fuzzy Time Series analysis. Even though forecasting methods have advanced applications in the last few decades, Fuzzy Time Series are common and have a lot of interest because they do not require any statistical assumptions on time series data.  Previous research has employed Fuzzy Time Series models to forecast enrollment statistics, stock prices, exchange rates, etc.  The major goal of This work is a comparative study of some different methods of forecasting the Fuzzy Time Series among which are the Markov Chain, Chen, and Cheng for Ghabbour Autocars data.  Seven statistical criteria have been used for investigating the accuracy of the models. All the calculations were performed using the R software system using the AnalyzeTS R package. The Markov-chain fuzzy time series model showed the highest performance (in all metrics); for instance, in RMSE, MAPE, and U-statistics are 0.013, 0.116, and 1.05 respectively.

DOI

10.21608/acj.2023.294123

Keywords

Fuzzy Time Series, Markov Chain methods, Chen methods, Cheng methods, MSE, RMSE and R software

Authors

First Name

Mona Mahmoud Samy

Last Name

Abo El Nasr

MiddleName

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Affiliation

Lecturer in Faculty of Commerce Mansoura University

Email

m_eldeep@mans.edu.eg

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Orcid

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Volume

60

Article Issue

2

Related Issue

40705

Issue Date

2023-03-01

Receive Date

2023-01-03

Publish Date

2023-03-01

Page Start

33

Page End

45

Print ISSN

2682-4183

Online ISSN

2682-4191

Link

https://acjalexu.journals.ekb.eg/article_294123.html

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https://acjalexu.journals.ekb.eg/service?article_code=294123

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2

Type

المقالة الأصلية

Type Code

759

Publication Type

Journal

Publication Title

مجلة جامعة الإسکندرية للعلوم الإدارية

Publication Link

https://acjalexu.journals.ekb.eg/

MainTitle

Fuzzy Time Series Forecasting: Chen, Markov Chain and Cheng Models

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Article

Created At

24 Dec 2024