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
An improved fuzzy time series forecasting method using trapezoidal fuzzy numbers
Fuzzy Optimization and Decision Making
Expert Systems with Applications: An International Journal
A SIMPLE TIME VARIANT METHOD FOR FUZZY TIME SERIES FORECASTING
Cybernetics and Systems
A computational method of forecasting based on fuzzy time series
Mathematics and Computers in Simulation
Expert Systems with Applications: An International Journal
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
Forecasting shanghai composite index based on fuzzy time series and improved C-fuzzy decision trees
Expert Systems with Applications: An International Journal
Forecasting model selection through out-of-sample rolling horizon weighted errors
Expert Systems with Applications: An International Journal
High-order fuzzy-neuro expert system for time series forecasting
Knowledge-Based Systems
An efficient time series forecasting model based on fuzzy time series
Engineering Applications of Artificial Intelligence
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This paper presents a computational method of forecasting based on high-order fuzzy time series. The developed computational method provides a better approach to overcome the drawback of existing high-order fuzzy time series models. Its simplicity lies with the use of differences in consecutive values of various orders as forecasting parameter and a w-step fuzzy predictor in place of complicated computations of fuzzy logical relations. The objective of the present study is to examine the suitability of various high-order fuzzy time series models in forecasting. The general suitability of the developed method has been tested by implementing it in the forecasting of student enrollments of the University of Alabama and in the forecasting of crop (Lahi) production, a case of high uncertainty in time series data. The results obtained have been compared in terms of average error of forecast to show superiority of the proposed model.