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
Time Series Analysis: Forecasting and Control
Time Series Analysis: Forecasting and Control
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
Fuzzy relation analysis in fuzzy time series model
Computers & Mathematics with Applications
A note on fuzzy time-series model
Fuzzy Sets and 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 measure based approach to the fusion of possibilistic and probabilistic uncertainty
Fuzzy Optimization and Decision Making
Fuzzy based trend mapping and forecasting for time series data
Expert Systems with Applications: An International Journal
Two new time-variant methods for fuzzy time series forecasting
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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Since Song and Chissom (Fuzzy Set Syst 54:1---9, 1993a) first proposed the structure of fuzzy time series forecast, researchers have devoted themselves to related studies. Among these studies, Hwang et al. (Fuzzy Set Syst 100:217---228, 1998) revised Song and Chissom's method, and generated better forecasted results. In their method, however, several factors that affect the accuracy of forecast are not taken into consideration, such as levels of window base, length of interval, degrees of membership values, and the existence of outliers. Focusing on these factors, this study proposes an improved fuzzy time series forecasting method. The improved method can provide decision-makers with more precise forecasted values. Two numerical examples are employed to illustrate the proposed method, as well as to compare the forecasting accuracy of the proposed method with that of two fuzzy forecasting methods. The results of the comparison indicate that the proposed method produces more accurate forecasting results.