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
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
Deterministic fuzzy time series model for forecasting enrollments
Computers & Mathematics with Applications
Fuzzy time-series based on adaptive expectation model for TAIEX forecasting
Expert Systems with Applications: An International Journal
Multi-attribute fuzzy time series method based on fuzzy clustering
Expert Systems with Applications: An International Journal
A bivariate fuzzy time series model to forecast the TAIEX
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Optimal adaptive fuzzy control for a class of unknown nonlinear systems
WSEAS Transactions on Systems and Control
WSEAS TRANSACTIONS on SYSTEMS
Application of fuzzy time series models for forecasting the amount of Taiwan export
Expert Systems with Applications: An International Journal
Fuzzy relation analysis in fuzzy time series model
Computers & Mathematics with Applications
Fuzzy reasoning and fuzzy relational equations
Fuzzy Sets and Systems
An application of fuzzy time series to improve ISE forecasting
WSEAS Transactions on Mathematics
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
Hi-index | 0.00 |
Prediction is a critical component in decision-making process for business management. Fuzzy Markov model is a common approach for dealing with the prediction of time series. However, not many studies devoted their attention to the effect of the parameters on model fitting for fuzzy Markov model. In the paper, we examine the prediction ability for fuzzy Markov model, based on the data of Taiwan's exports and foreign exchange rate. The empirical results indicate that fuzzy Markov model performs better for longer period forecasting; moreover, neither increment information nor increasing window basis would improve the performance for fuzzy Markov model. An advantage of the paper provides a beneficial knowledge when using Markov model for prediction.