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
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
A FCM-based deterministic forecasting model for fuzzy time series
Computers & Mathematics with Applications
Expert Systems with Applications: An International Journal
AN ENHANCED DETERMINISTIC FUZZY TIME SERIES FORECASTING MODEL
Cybernetics and Systems
An integrated fuzzy time series forecasting system
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
A neural network-based fuzzy time series model to improve forecasting
Expert Systems with Applications: An International Journal
Adaptive-expectation based multi-attribute FTS model for forecasting TAIEX
Computers & Mathematics with Applications
Deterministic vector long-term forecasting for fuzzy time series
Fuzzy Sets and Systems
An adaptive ordered fuzzy time series with application to FOREX
Expert Systems with Applications: An International Journal
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
Expert Systems with Applications: An International Journal
WSEAS Transactions on Circuits and Systems
Fuzzy time series model incorporating predictor variables and interval partition
WSEAS Transactions on Mathematics
An efficient time series forecasting model based on fuzzy time series
Engineering Applications of Artificial Intelligence
A seasonal discrete grey forecasting model for fashion retailing
Knowledge-Based Systems
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The fuzzy time series has recently received increasing attention because of its capability of dealing with vague and incomplete data. There have been a variety of models developed to either improve forecasting accuracy or reduce computation overhead. However, the issues of controlling uncertainty in forecasting, effectively partitioning intervals, and consistently achieving forecasting accuracy with different interval lengths have been rarely investigated. This paper proposes a novel deterministic forecasting model to manage these crucial issues. In addition, an important parameter, the maximum length of subsequence in a fuzzy time series resulting in a certain state, is deterministically quantified. Experimental results using the University of Alabama's enrollment data demonstrate that the proposed forecasting model outperforms the existing models in terms of accuracy, robustness, and reliability. Moreover, the forecasting model adheres to the consistency principle that a shorter interval length leads to more accurate results.