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
The use of Kernel set and sample memberships in the identification of nonlinear time series
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Expert Systems with Applications: An International Journal
Temperature prediction using fuzzy time series
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
Fuzzy time series model incorporating predictor variables and interval partition
WSEAS Transactions on Mathematics
Hi-index | 12.05 |
The study proposed Traditional Time Series Method (ARIMA model and Vector ARMA model) and Fuzzy Time Series Method (Two-factor model, Heuristic model, and Markov model) for the forecasting problem. The real world case of Taiwan exports is employed for models' test to compare the forecasting ability among models and to examine the effects of different lengths of interval and increment information on the forecasting error of models. The results indicate that Fuzzy Time Series Method performs better forecasting ability in short-term period prediction, especially Heuristic model. The ARIMA model generates smaller forecasting errors in longer experiment time period. Nevertheless, introducing increment information is not necessarily in improving the forecasting ability of fuzzy time series. As a result, it is more convenient to use the fuzzy time series method in the limited information and urgent decision-making circumstance.