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
A bivariate fuzzy time series model to forecast the TAIEX
Expert Systems with Applications: An International Journal
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
A neural network-based fuzzy time series model to improve forecasting
Expert Systems with Applications: An International Journal
Forecast Combination by Using Artificial Neural Networks
Neural Processing Letters
Fuzzy time series forecasting method based on Gustafson-Kessel fuzzy clustering
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Revenue forecasting using a least-squares support vector regression model in a fuzzy environment
Information Sciences: an International Journal
A new linear & nonlinear artificial neural network model for time series forecasting
Decision Support Systems
A New Multiplicative Seasonal Neural Network Model Based on Particle Swarm Optimization
Neural Processing Letters
Introducing polynomial fuzzy time series
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
| Hi-index | 12.06 |
In the literature, there have been many studies using fuzzy time series for the purpose of forecasting. The most studied model is the first order fuzzy time series model. In this model, an observation of fuzzy time series is obtained by using the previous observation. In other words, only the first lagged variable is used when constructing the first order fuzzy time series model. Therefore, this model can not be sufficient for some time series such as seasonal time series which is an important class in time series models. Besides, the time series encountered in real life have not only autoregressive (AR) structure but also moving average (MA) structure. The fuzzy time series models available in the literature are AR structured and are not appropriate for MA structured time series. In this paper, a hybrid approach is proposed in order to analyze seasonal fuzzy time series. The proposed hybrid approach is based on partial high order bivariate fuzzy time series forecasting model which is first introduced in this paper. The order of this model is determined by utilizing Box-Jenkins method. In order to show the efficiency of the proposed hybrid method, real time series are analyzed with this method. The results obtained from the proposed method are compared with the other methods. As a result, it is observed that more accurate results are obtained from the proposed hybrid method.