Fuzzy Clustering To Classify Several Time Series Models With Fractional Brownian Motion Errors
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Date
2021
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier
Open Access Color
GOLD
Green Open Access
No
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No
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Impulse
Top 10%
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Top 10%
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Top 10%
Abstract
In real world problems, scientists aim to classify and cluster several time series processes that can be used for a dataset. In this research, for the first time, based on fuzzy clustering method, an approach is applied to classify and cluster several time series models with fractional Brownian motion errors as candidates to fit on a dataset. The ability of the introduced technique is studied using simulation and real world example. (C) 2020 The Authors. Published by Elsevier B.V.
Description
S. Band, Shahab/0000-0001-6109-1311; Noman Qasem, Sultan/0000-0002-6575-161X
Keywords
Classification, Fuzzy Clustering, Fractional Brownian Motion, Non-Stationary, Stationary, Time Series, Rdi, Non-stationary, Time series, Stationary, Fuzzy clustering, TA1-2040, Classification, Engineering (General). Civil engineering (General), Fractional Brownian motion
Fields of Science
0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 0101 mathematics, 01 natural sciences
Citation
Mahmoudi, Mohammad Reza...et al. (2021). "Fuzzy clustering to classify several time series models with fractional Brownian motion errors", Alexandria Engineering Journal, Vol. 60, No. 1, pp. 1137-1145.
WoS Q
Q1
Scopus Q
Q1
OpenCitations Citation Count
21
Source
Alexandria Engineering Journal
Volume
60
Issue
1
Start Page
1137
End Page
1145
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Citations
CrossRef : 21
Scopus : 21
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Mendeley Readers : 17
SCOPUS™ Citations
21
checked on Feb 23, 2026
Web of Science™ Citations
20
checked on Feb 23, 2026
Page Views
3
checked on Feb 23, 2026
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