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 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
Handling forecasting problems based on two-factors high-order fuzzy time series
IEEE Transactions on Fuzzy Systems
A new architecture selection method based on tabu search for artificial neural networks
Expert Systems with Applications: An International Journal
Fuzzy time series forecasting method based on Gustafson-Kessel fuzzy clustering
Expert Systems with Applications: An International Journal
Using artificial neural network models in stock market index prediction
Expert Systems with Applications: An International Journal
A new time-invariant fuzzy time series forecasting method based on genetic algorithm
Advances in Fuzzy Systems - Special issue on Fuzzy Function, Relations, and Fuzzy Transforms (2012)
Using multiplicative neuron model to establish fuzzy logic relationships
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Advances in Fuzzy Systems
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Fuzzy time series methods have been recently becoming very popular in forecasting. These methods can be categorized into two subclasses that are univariate and multivariate approaches. It is a known fact that real time series data can actually be affected by many factors. In this case, the using multivariate fuzzy time series forecasting model can be more reasonable in order to get more accurate forecasts. To obtain fuzzy forecasts when multivariate fuzzy time series approach is adopted, the most applied method is using tables of fuzzy relations. However, employing this method is a computationally though task. In this study, we introduce a new method that does not require using fuzzy logic relation tables in order to determine fuzzy relationships. Instead, a feed forward artificial neural network is employed to determine fuzzy relationships. The proposed method is applied to the time series data of the total number of annual car road accidents casualties in Belgium from 1974 to 2004 and a comparison is made between our proposed method and the methods proposed by Jilani and Burney [Jilani, T. A., & Burney, S. M. A. (2008). Multivariate stochastic fuzzy forecasting models. Expert Systems with Applications, 35, 691-700] and Lee et al. [Lee, L.-W., Wang, L.-H., Chen, S.-M., & Leu, Y.-H. (2006). Handling forecasting problems based on two factors high order fuzzy time series. IEEE Transactions on Fuzzy Systems, 14, 468-477].