Fuzzy time series and its models
Fuzzy Sets and Systems
Forecasting enrollments with fuzzy time series—part I
Fuzzy Sets and Systems
Forecasting enrollments based on fuzzy time series
Fuzzy Sets and Systems
Multi-attribute fuzzy time series method based on fuzzy clustering
Expert Systems with Applications: An International Journal
Forecasting in high order fuzzy times series by using neural networks to define fuzzy relations
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Incorporation of a Regularization Term to Control Negative Correlation in Mixture of Experts
Neural Processing Letters
A New Multiplicative Seasonal Neural Network Model Based on Particle Swarm Optimization
Neural Processing Letters
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One of the efficient ways for obtaining accurate forecasts is usage of forecast combination method. This approach consists of combining different forecast values obtained from different forecasting models. Also artificial neural networks and fuzzy time series approaches have proved their success in the field of forecasting. In this study, a new forecast combination approach based on artificial neural networks is proposed. The forecasts obtain from different fuzzy time series models are combined by utilizing artificial neural networks. The proposed method is applied to index of Istanbul stock exchange (IMKB) time series and the results are compared to other forecast combination methods available in the literature. As a result of the implementation, it is seen that the proposed forecast combination approach produces better forecasts than those produced by other methods.