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
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
A combined forecasting approach based on fuzzy soft sets
Journal of Computational and Applied Mathematics
An improved fuzzy forecasting method for seasonal time series
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
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There have been many recently proposed methods for forecasting fuzzy time series. Most of them are, however, for non-seasonal fuzzy time series. A definition of seasonal fuzzy time series was firstly given by Song Q. Song, Seasonal forecasting in fuzzy time series, Fuzzy Sets and Systems 107 1999, 235--236. In his paper, the model was a first order seasonal fuzzy time series. However, real time series behave very rarely in a first order seasonal fuzzy time series structure. There is a need for modeling high order seasonal structures because their structure generally is more complicated. We make a definition for a high order seasonal fuzzy time series and propose a new approach based on artificial neural networks for forecasting a high order seasonal fuzzy time series. This proposed method is applied to the time series of the international tourism demand of Turkey. The results from this approach are compared to the results obtained from conventional seasonal fuzzy time series methods. From this comparisons we observe that the new method improve the forecasting accuracy.