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
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
An improved fuzzy time series forecasting method using trapezoidal fuzzy numbers
Fuzzy Optimization and Decision Making
Expert Systems with Applications: An International Journal
Deterministic fuzzy time series model for forecasting enrollments
Computers & Mathematics with Applications
A dynamic approach to adjusting lengths of intervals in fuzzy time series forecasting
Intelligent Data Analysis
Fuzzy relation analysis in fuzzy time series model
Computers & Mathematics with Applications
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
Deterministic vector long-term forecasting for fuzzy time series
Fuzzy Sets and Systems
A stochastic HMM-based forecasting model for fuzzy time series
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A vector forecasting model for fuzzy time series
Applied Soft Computing
Forecasting shanghai composite index based on fuzzy time series and improved C-fuzzy decision trees
Expert Systems with Applications: An International Journal
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The study of fuzzy time series has attracted great interest and is expected to expand rapidly. Various forecasting models including high-order models have been proposed to improve forecasting accuracy or reducing computational cost. However, there exist two important issues, namely, rule redundancy and high-order redundancy that have not yet been investigated. This article proposes a novel forecasting model to tackle such issues. It overcomes the major hurdle of determining the k-order in high-order models and is enhanced to allow the handling of multi-factor forecasting problems by removing the overhead of deriving all fuzzy logic relationships beforehand. Two novel performance evaluation metrics are also formally derived for comparing performances of related forecasting models. Experimental results demonstrate that the proposed forecasting model outperforms the existing models in efficiency.