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A Novel Hybrid Fuzzy Time Series Approach with Applications to Enrollments and Car Road Accidents

by Shehu Mohammed Yusuf, M.B. Mu'azu, O. Akinsanmi
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 129 - Number 2
Year of Publication: 2015
Authors: Shehu Mohammed Yusuf, M.B. Mu'azu, O. Akinsanmi
10.5120/ijca2015906852

Shehu Mohammed Yusuf, M.B. Mu'azu, O. Akinsanmi . A Novel Hybrid Fuzzy Time Series Approach with Applications to Enrollments and Car Road Accidents. International Journal of Computer Applications. 129, 2 ( November 2015), 37-44. DOI=10.5120/ijca2015906852

@article{ 10.5120/ijca2015906852,
author = { Shehu Mohammed Yusuf, M.B. Mu'azu, O. Akinsanmi },
title = { A Novel Hybrid Fuzzy Time Series Approach with Applications to Enrollments and Car Road Accidents },
journal = { International Journal of Computer Applications },
issue_date = { November 2015 },
volume = { 129 },
number = { 2 },
month = { November },
year = { 2015 },
issn = { 0975-8887 },
pages = { 37-44 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume129/number2/23047-2015906852/ },
doi = { 10.5120/ijca2015906852 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:23:45.673974+05:30
%A Shehu Mohammed Yusuf
%A M.B. Mu'azu
%A O. Akinsanmi
%T A Novel Hybrid Fuzzy Time Series Approach with Applications to Enrollments and Car Road Accidents
%J International Journal of Computer Applications
%@ 0975-8887
%V 129
%N 2
%P 37-44
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Fuzzy time series techniques are more suitable than traditional time series techniques in forecasting problems with linguistic values. Two shortcomings of existing fuzzy time series forecasting techniques are they lack persuasiveness in dealing with recurrent number of fuzzy relationships and assigning weights to elements of fuzzy rules in the defuzzification process. In this paper, a novel fuzzy time series technique based on fuzzy C-means clustering and particle swarm optimization is proposed to resolve these shortcomings. Fuzzy C-means clustering is adopted in the fuzzification process to objectively partition the universe of discourse. Then, particle swarm optimization is adopted to assign optimal weights to elements of fuzzy rules. Actual yearly enrollments at the University of Alabama and yearly deaths in car road accidents in Belgium are used as benchmark data. The forecasting results showed that the proposed method outperformed other existing methods.

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Index Terms

Computer Science
Information Sciences

Keywords

Forecasting Fuzzy Time Series Fuzzy C-means clustering Particle Swarm Optimization.

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