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
Handling forecasting problems using fuzzy time series
Fuzzy Sets and Systems
Information Sciences: an International Journal
Forecasting enrollments using high-order fuzzy time series and genetic algorithms: Research Articles
International Journal of Intelligent Systems
Temperature prediction using fuzzy time series
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Ratio-based lengths of intervals to improve fuzzy time series forecasting
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Handling forecasting problems based on two-factors high-order fuzzy time series
IEEE Transactions on Fuzzy Systems
A computational method of forecasting based on fuzzy time series
Mathematics and Computers in Simulation
A computational method of forecasting based on high-order fuzzy time series
Expert Systems with Applications: An International Journal
Forecasting enrollments using automatic clustering techniques and fuzzy logical relationships
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
A neural network-based fuzzy time series model to improve forecasting
Expert Systems with Applications: An International Journal
A new method to forecast the TAIEX based on fuzzy time series
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
A new method for forecasting the TAIEX based on high-order fuzzy logical relationships
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
An application of fuzzy time series to improve ISE forecasting
WSEAS Transactions on Mathematics
Fuzzy forecasting based on fuzzy-trend logical relationship groups
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Handling forecasting problems based on high-order fuzzy logical relationships
Expert Systems with Applications: An International Journal
Forecasting short-term trends of stock markets based on fuzzy frequent pattern tree
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
Expert Systems with Applications: An International Journal
A generalized method for forecasting based on fuzzy time series
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
The adaptive fuzzy time series model with an application to Taiwan's tourism demand
Expert Systems with Applications: An International Journal
Forecasting shanghai composite index based on fuzzy time series and improved C-fuzzy decision trees
Expert Systems with Applications: An International Journal
BICA'12 Proceedings of the 5th WSEAS congress on Applied Computing conference, and Proceedings of the 1st international conference on Biologically Inspired Computation
A Critical Evaluation of Computational Methods of Forecasting Based on Fuzzy Time Series
International Journal of Decision Support System Technology
Information Sciences: an International Journal
An efficient time series forecasting model based on fuzzy time series
Engineering Applications of Artificial Intelligence
Determination of temporal information granules to improve forecasting in fuzzy time series
Expert Systems with Applications: An International Journal
Introducing polynomial fuzzy time series
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Hi-index | 12.07 |
In our daily life, we often use some forecasting techniques to predict weather, temperature, stock, earthquake, economy, etc. Based on these forecasting results, we can prevent damages to occur or get benefits from the forecasting activities. In fact, an event in the real-world can be affected by many factors. The more the facts we consider, the higher the forecasting accuracy rate. Moreover the length of each interval in the universe of discourse also affects the forecasting results. In this paper, we present a new method to predict the temperature and the Taiwan Futures Exchange (TAIFEX), based on automatic clustering techniques and two-factors high-order fuzzy time series. The proposed method gets higher average forecasting accuracy rates than the existing methods.