Introduction to artificial neural systems
Introduction to artificial neural systems
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
Fuzzy Sets and Systems
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
A fuzzy seasonal ARIMA model for forecasting
Fuzzy Sets and Systems - Information processing
Expert Systems with Applications: An International Journal
A dynamic approach to adjusting lengths of intervals in fuzzy time series forecasting
Intelligent Data Analysis
Multi-attribute fuzzy time series method based on fuzzy clustering
Expert Systems with Applications: An International Journal
A bivariate fuzzy time series model to forecast the TAIEX
Expert Systems with Applications: An International Journal
Multivariate stochastic fuzzy forecasting models
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
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
Handling forecasting problems based on two-factors high-order fuzzy time series
IEEE Transactions on Fuzzy Systems
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Fuzzy approach and artificial neural networks become effective tool for researchers by forecasting fuzzy time series. The relation of these has advantage to improve forecasting performance especially in handling nonlinear systems. Hence, in this study we aimed to handle a nonlinear problem to apply neural network-based fuzzy time series model. Differing from previous studies, we used various degrees of membership in establishing fuzzy relationships and we performed different neural network models to improve forecasting performance. To demonstrate comparison between these models we used a data set of exchange rate of Turkish Liras (TL) to Euro for the years 2005-2009. Empirical results show that the multilayer perceptron is the best to forecast fuzzy time series in most commonly used artificial neural network models.