A New Fuzzy Time Series Model Based on Fuzzy C-Regression Model

This study proposes a new fuzzy time series model based on Fuzzy C-Regression Model clustering algorithm (FCRMF). There are two major superiorities of FCRMF in comparison with existing fuzzy time series model based on fuzzy clustering. The first one is that FCRMF partitions data set by taking into a...

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Vydáno v:	International journal of fuzzy systems Ročník 20; číslo 6; s. 1872 - 1887
Hlavní autor:	Güler Dincer, Nevin
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2018 Springer Nature B.V
Témata:	Algorithms Artificial Intelligence Clustering Computational Intelligence Data points Engineering Forecasting Fuzzy sets Goodness of fit Management Science Methods Operations Research Performance evaluation Production methods Regression models Statistical analysis Time series Universe Fuzzy time series Fuzzy clustering Fuzzy C-Regression Model Forecasting Time series
ISSN:	1562-2479, 2199-3211
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Abstract	This study proposes a new fuzzy time series model based on Fuzzy C-Regression Model clustering algorithm (FCRMF). There are two major superiorities of FCRMF in comparison with existing fuzzy time series model based on fuzzy clustering. The first one is that FCRMF partitions data set by taking into account the relationship between the classical time series and lagged values, and thus, it gives the more realistic clustering results. The second one is that FCRMF produces different forecasting values for each data point, while the other fuzzy time series methods produce same forecasting values for many data points. In order to validate the forecasting performance of proposed method and compare it to the other fuzzy time series methods based on fuzzy clustering, six simulation studies and two real-time examples are carried out. According to goodness-of-fit measures, it is observed that FCRMF provides the best forecasting results, especially in cases when time series are not stationary. When considering that fuzzy time series was proposed especially for cases that time series do not satisfy statistical assumptions such as the stationary, this is very important advantage.
AbstractList	This study proposes a new fuzzy time series model based on Fuzzy C-Regression Model clustering algorithm (FCRMF). There are two major superiorities of FCRMF in comparison with existing fuzzy time series model based on fuzzy clustering. The first one is that FCRMF partitions data set by taking into account the relationship between the classical time series and lagged values, and thus, it gives the more realistic clustering results. The second one is that FCRMF produces different forecasting values for each data point, while the other fuzzy time series methods produce same forecasting values for many data points. In order to validate the forecasting performance of proposed method and compare it to the other fuzzy time series methods based on fuzzy clustering, six simulation studies and two real-time examples are carried out. According to goodness-of-fit measures, it is observed that FCRMF provides the best forecasting results, especially in cases when time series are not stationary. When considering that fuzzy time series was proposed especially for cases that time series do not satisfy statistical assumptions such as the stationary, this is very important advantage.
Author	Güler Dincer, Nevin
Author_xml	– sequence: 1 givenname: Nevin surname: Güler Dincer fullname: Güler Dincer, Nevin email: nguler@mu.edu.tr organization: Department of Statistics, Faculty of Science, University of Muğla Sıtkı Koçman
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Snippet	This study proposes a new fuzzy time series model based on Fuzzy C-Regression Model clustering algorithm (FCRMF). There are two major superiorities of FCRMF in...
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Title	A New Fuzzy Time Series Model Based on Fuzzy C-Regression Model
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