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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Published in:International journal of fuzzy systems Vol. 20; no. 6; pp. 1872 - 1887
Main Author: Güler Dincer, Nevin
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2018
Springer Nature B.V
Subjects:
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
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Keywords Fuzzy time series
Fuzzy clustering
Fuzzy C-Regression Model
Forecasting
Time series
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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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SubjectTerms 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
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Title A New Fuzzy Time Series Model Based on Fuzzy C-Regression Model
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