Fuzzy reasoning and fuzzy relational equations
Fuzzy Sets and Systems
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
Adaptive learning defuzzification techniques and applications
Fuzzy Sets and Systems
Handling forecasting problems using fuzzy time series
Fuzzy Sets and Systems
Application of Fuzzy Logic to Approximate Reasoning Using Linguistic Synthesis
IEEE Transactions on Computers
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
An integrated fuzzy time series forecasting system
Expert Systems with Applications: An International Journal
A computational method of forecasting based on high-order fuzzy time series
Expert Systems with Applications: An International Journal
An improved fuzzy forecasting method for seasonal time series
Expert Systems with Applications: An International Journal
Fuzzy based trend mapping and forecasting for time series data
Expert Systems with Applications: An International Journal
Partitions based computational method for high-order fuzzy time series forecasting
Expert Systems with Applications: An International Journal
A Critical Evaluation of Computational Methods of Forecasting Based on Fuzzy Time Series
International Journal of Decision Support System Technology
Introducing polynomial fuzzy time series
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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Forecasting using fuzzy time series models needs computations of fuzzy relations in adjacent observations of time series data. In view of getting better forecasted values, these fuzzy relations have been considered as time invariant and time variant, and have been computed in several ways. However, the complication lies with the various rules developed for obtaining these fuzzy relations and then the defuzzification process. In this article, we propose a simple time variant method for time series forecasting. It uses the difference operator and the values obtained have been used for developing fuzzy rules for forecast. We develop algorithms to forecast enrollments of the University of Alabama and compared them with existing methods. The method has been also implemented to forecast rice production of Pantnagar (farm), India. The computational algorithms of the proposed method are simple and provide higher accuracy in forecasting.