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
Fuzzy stochastic fuzzy time series and its models
Fuzzy Sets and Systems
Handling forecasting problems using fuzzy time series
Fuzzy Sets and Systems
Compliance-based structural damage measure and its sensitivity to uncertainties
Computers and Structures
Engineering computation under uncertainty - Capabilities of non-traditional models
Computers and Structures
A Fuzzy Asymmetric GARCH model applied to stock markets
Information Sciences: an International Journal
Deterministic vector long-term forecasting for fuzzy time series
Fuzzy Sets and Systems
A stochastic HMM-based forecasting model for fuzzy time series
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Revenue forecasting using a least-squares support vector regression model in a fuzzy environment
Information Sciences: an International Journal
| Hi-index | 0.00 |
Numerical methods for the prediction of uncertain structural responses with the aid of fuzzy time series are presented. Uncertain data, uncertain measured actions, and uncertain structural responses over time are considered as time series comprised of fuzzy data. Uncertain data are described by means of a new incremental fuzzy representation, which permits a complete and accurate estimation of uncertainty. The fuzzy time series are regarded as realizations of a fuzzy random process. Methods for identification and quantification of the underlying fuzzy random process are developed. The concepts of model-free and of model-based forecasting are addressed. These concepts enable the prediction of data in the form of optimal forecasts, fuzzy forecast intervals, and fuzzy random forecasts. The algorithms are demonstrated by way of practical examples.