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
Forecasting enrollments using high-order fuzzy time series and genetic algorithms: Research Articles
International Journal of Intelligent Systems
Fuzzy time-series based on adaptive expectation model for TAIEX forecasting
Expert Systems with Applications: An International Journal
A SIMPLE TIME VARIANT METHOD FOR FUZZY TIME SERIES FORECASTING
Cybernetics and Systems
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
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
Forecasting stock index price based on M-factors fuzzy time series and particle swarm optimization
International Journal of Approximate Reasoning
Two new time-variant methods for fuzzy time series forecasting
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Hi-index | 12.05 |
Several time-variant fuzzy time series models have been developed during the last decade. These models usually focus on forecasting stationary of trend time series, but they are not suitable for forecasting seasonal time series. Furthermore, several factors that affect the forecasting accuracy are not carefully examined, such as interval length, interval number, and level of window base. Aiming to solve these issues, the goal of this study is to develop an improved fuzzy time series forecasting method that can effectively deal with seasonal time series. The proposed method can determine appropriate length interval. Moreover, a systematic search algorithm is used to find the best window base. The proposed method can provide decision analysts with more precise forecasted values. Two numerical data sets are employed to illustrate the proposed method and to compare the forecasting accuracy between the proposed method and four fuzzy time series methods. The results of the comparison indicate that the proposed method produces more accurate forecasted results.