A new time-invariant fuzzy time series forecasting method based on genetic algorithm
type: Item
instance of: scholarly article
title: A new time-invariant fuzzy time series forecasting method based on genetic algorithm
zbMATH Open document ID: 1252.62092
DOI: 10.1155/2012/785709
author: Erol Egrioglu
published in: Advances in Fuzzy Systems
publication date: 2012-09-06T00:00:00Z
review text: Summary: In recent years, many fuzzy time series methods have been proposed in the literature. Some of these methods use the classical fuzzy set theory, which needs complex matricial operations in fuzzy time series methods. Because of this problem, many studies in the literature use fuzzy group relationship tables. Since the fuzzy relationship tables use order of fuzzy sets, the membership functions of fuzzy sets have not been taken into consideration. In this study, a new method that employs membership functions of fuzzy sets is proposed. The new method determines elements of a fuzzy relation matrix based on genetic algorithms. The proposed method uses first-order fuzzy time series forecasting model, and it is applied to the several data sets. As a result of implementation, it is obtained that the proposed method outperforms some methods in the literature.
Mathematics Subject Classification ID: 62M20
Mathematics Subject Classification ID: 62M86
Mathematics Subject Classification ID: 90C59
Mathematics Subject Classification ID: 62M10
zbMATH DE Number: 6078115
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2012/785709
OpenAlex ID: W2052369115
Wikidata QID: Q58698173
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description: scientific article; zbMATH DE number 6078115