Forecasting enrollments with fuzzy time series—part I
Fuzzy Sets and Systems
Essentials of fuzzy modeling and control
Essentials of fuzzy modeling and control
Fuzzy sets and fuzzy logic: theory and applications
Fuzzy sets and fuzzy logic: theory and applications
Forecasting enrollments based on fuzzy time series
Fuzzy Sets and Systems
Fuzzy set theory—and its applications (3rd ed.)
Fuzzy set theory—and its applications (3rd ed.)
Iterative inversion of fuzzified neural networks
IEEE Transactions on Fuzzy Systems
Handling forecasting problems based on two-factors high-order fuzzy time series
IEEE Transactions on Fuzzy Systems
Neural networks that learn from fuzzy if-then rules
IEEE Transactions on Fuzzy Systems
A new approach based on artificial neural networks for high order multivariate fuzzy time series
Expert Systems with Applications: An International Journal
A neural network-based fuzzy time series model to improve forecasting
Expert Systems with Applications: An International Journal
Conceptual fuzzy temporal relational model (FTRM) for patient data
WSEAS Transactions on Information Science and Applications
A heuristic time-invariant model for fuzzy time series forecasting
Expert Systems with Applications: An International Journal
Adaptive time-variant models for fuzzy-time-series forecasting
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Multivariate fuzzy forecasting based on fuzzy time series and automatic clustering techniques
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
BICA'12 Proceedings of the 5th WSEAS congress on Applied Computing conference, and Proceedings of the 1st international conference on Biologically Inspired Computation
Advances in Fuzzy Systems
| Hi-index | 12.07 |
In this paper, we have presented two new multivariate fuzzy time series forecasting methods. These methods assume m-factors with one main factor of interest. Stochastic fuzzy dependence of order k is assumed to define general methods of multivariate fuzzy time series forecasting and control. These new methods are applied for forecasting total number of car road accidents casualties in Belgium using four secondary factors. Practically, in most of the situations, actuaries are interested in analysis of the patterns of casualties in road accidents. Such type of analysis supports in deciding approximate risk classification and forecasting for each area of a city. This directly affects the underwriting process and adjustment of insurance premium, based on risk intensity for each area. National Institute of Statistics, Belgium provides risk intensity based classification of each city. Thus, this work provides support in deciding the appropriate risk associated with an insured in a particular area.