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
A new fuzzy time-series model of fuzzy number observations
Fuzzy Sets and Systems
Forecasting enrollments based on fuzzy time series
Fuzzy Sets and Systems
Fuzzy sets, fuzzy logic, applications
Fuzzy sets, fuzzy logic, applications
Handling forecasting problems using fuzzy time series
Fuzzy Sets and Systems
Fuzzy Control and Fuzzy Systems
Fuzzy Control and Fuzzy Systems
Time Series Analysis: Forecasting and Control
Time Series Analysis: Forecasting and Control
Temperature prediction using fuzzy time series
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
ScaleNet-multiscale neural-network architecture for time series prediction
IEEE Transactions on Neural Networks
Expert Systems with Applications: An International Journal
A new approach for determining the length of intervals for fuzzy time series
Applied Soft Computing
AN ENHANCED DETERMINISTIC FUZZY TIME SERIES FORECASTING MODEL
Cybernetics and Systems
A neural network-based fuzzy time series model to improve forecasting
Expert Systems with Applications: An International Journal
An application of fuzzy time series to improve ISE forecasting
WSEAS Transactions on Mathematics
A new approach based on the optimization of the length of intervals in fuzzy time series
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
A generalized method for forecasting based on fuzzy time series
Expert Systems with Applications: An International Journal
Forecasting neural network-based fuzzy time series with different neural network models
GAVTASC'11 Proceedings of the 11th WSEAS international conference on Signal processing, computational geometry and artificial vision, and Proceedings of the 11th WSEAS international conference on Systems theory and scientific computation
Forecasting shanghai composite index based on fuzzy time series and improved C-fuzzy decision trees
Expert Systems with Applications: An International Journal
Hi-index | 0.01 |
Fuzzy time series models have been proposed to model linguistic observations and have been extended to model numerical observations as well. Many factors are believed to affect fuzzy time series forecasting. The formulation of fuzzy relationships and the lengths of intervals for observations are considered two of them. Hence, how to cover both issues simultaneously is important for the improvement of forecasting results. This study proposes a dynamic approach to adjusting lengths of intervals in fuzzy time series forecasting, thus capturing fuzzy relationships more appropriately. These fuzzy relationships can then be used to improve forecasting. Enrollment and stock index forecasting are used to demonstrate the effectiveness of the dynamic approach. Empirical results show that this dynamic approach can be applied to improve fuzzy time series forecasting.