Fuzzy time series and its models
Fuzzy Sets and Systems
Forecasting enrollments with fuzzy time series—part I
Fuzzy Sets and Systems
Forecasting enrollments with fuzzy time series—part II
Fuzzy Sets and Systems
A comparison of fuzzy forecasting and Markov modeling
Fuzzy Sets and Systems
Forecasting enrollments based on fuzzy time series
Fuzzy Sets and Systems
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Fuzzy time-series based on adaptive expectation model for TAIEX forecasting
Expert Systems with Applications: An International Journal
Multi-attribute fuzzy time series method based on fuzzy clustering
Expert Systems with Applications: An International Journal
A FCM-based deterministic forecasting model for fuzzy time series
Computers & Mathematics with Applications
Forecasting in high order fuzzy times series by using neural networks to define fuzzy relations
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A new approach for determining the length of intervals for fuzzy time series
Applied Soft Computing
Forecasting TAIFEX based on fuzzy time series and particle swarm optimization
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Fuzzy relation analysis in fuzzy time series model
Computers & Mathematics with Applications
Temperature prediction and TAIFEX forecasting based on fuzzy relationships and MTPSO techniques
Expert Systems with Applications: An International Journal
A neural network-based fuzzy time series model to improve forecasting
Expert Systems with Applications: An International Journal
Finding an optimal interval length in high order fuzzy time series
Expert Systems with Applications: An International Journal
Mathematics and Computers in Simulation
Fuzzy time series prediction using hierarchical clustering algorithms
Expert Systems with Applications: An International Journal
A new approach based on the optimization of the length of intervals in fuzzy time series
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Fuzzy time series forecasting method based on Gustafson-Kessel fuzzy clustering
Expert Systems with Applications: An International Journal
Multivariate fuzzy forecasting based on fuzzy time series and automatic clustering techniques
Expert Systems with Applications: An International Journal
Ratio-based lengths of intervals to improve fuzzy time series forecasting
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Generalized dynamical fuzzy model for identification and prediction
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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In the analysis of time invariant fuzzy time series, fuzzy logic group relationships tables have been generally preferred for determination of fuzzy logic relationships. The reason of this is that it is not need to perform complex matrix operations when these tables are used. On the other hand, when fuzzy logic group relationships tables are exploited, membership values of fuzzy sets are ignored. Thus, in defiance of fuzzy set theory, fuzzy sets' elements with the highest membership value are only considered. This situation causes information loss and decrease in the explanation power of the model. To deal with these problems, a novel time invariant fuzzy time series forecasting approach is proposed in this study. In the proposed method, membership values in the fuzzy relationship matrix are computed by using particle swarm optimization technique. The method suggested in this study is the first method proposed in the literature in which particle swarm optimization algorithm is used to determine fuzzy relations. In addition, in order to increase forecasting accuracy and make the proposed approach more systematic, the fuzzy c-means clustering method is used for fuzzification of time series in the proposed method. The proposed method is applied to well-known time series to show the forecasting performance of the method. These time series are also analyzed by using some other forecasting methods available in the literature. Then, the results obtained from the proposed method are compared to those produced by the other methods. It is observed that the proposed method gives the most accurate forecasts.