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
Forecasting enrollments based on fuzzy time series
Fuzzy Sets and Systems
Two strategies to avoid overfitting in feedforward networks
Neural Networks
Handling forecasting problems using fuzzy time series
Fuzzy Sets and Systems
Forecasting enrollments using high-order fuzzy time series and genetic algorithms: Research Articles
International Journal of Intelligent Systems
Fuzzy time-series based on adaptive expectation model for TAIEX forecasting
Expert Systems with Applications: An International Journal
A SIMPLE TIME VARIANT METHOD FOR FUZZY TIME SERIES FORECASTING
Cybernetics and Systems
Expert Systems with Applications: An International Journal
A new approach for determining the length of intervals for fuzzy time series
Applied Soft Computing
Expert Systems with Applications: An International Journal
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
Temperature prediction and TAIFEX forecasting based on fuzzy relationships and MTPSO techniques
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
Computers and Operations Research
Handling forecasting problems based on two-factors high-order fuzzy time series
IEEE Transactions on Fuzzy Systems
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Using polynomial concept and non-liner optimization enhanced the performance of Chen's 1996 and Yu's 2005b methods as the two frequently used methods in fuzzy time series model. To this end, polynomial schemes were given to each fuzzy logical relationship groups that had been established through forecast process to establish non-linear optimization systems. The optimal solutions of this system were applied in corresponding steps of algorithms to obtain new weights. To validate model reliability and its effectiveness, the forecasts of two huge databases namely 5 years Taiwan's stock index and 2010 load data of Power Supply Company in Johor Bahru in Malaysia were then exposed to the proposed model. Next, the forecasts were compared with real values in testing datasets. The evaluation of measuring criteria namely RMSEs and MAPEs showed that the proposed model could produce accurate forecast compared with the Chen's and Yu's method in fuzzy time series. The implication of this study is to generalize the results to other fuzzy time series models.