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
Forecasting enrollments based on fuzzy time series
Fuzzy Sets and Systems
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Time series prediction with single multiplicative neuron model
Applied Soft Computing
PSO-based single multiplicative neuron model for time series prediction
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
A new approach based on artificial neural networks for high order multivariate fuzzy time series
Expert Systems with Applications: An International Journal
Temperature prediction and TAIFEX forecasting based on fuzzy relationships and MTPSO techniques
Expert Systems with Applications: An International Journal
A neural network-based fuzzy time series model to improve forecasting
Expert Systems with Applications: An International Journal
Mathematics and Computers in Simulation
Forecasting stock index price based on M-factors fuzzy time series and particle swarm optimization
International Journal of Approximate Reasoning
| Hi-index | 12.05 |
Determination of fuzzy logic relationships between observations is quite effective on the forecasting performance of fuzzy time series approaches. In various studies available in the literature, it has been seen that utilizing artificial neural networks for establishing fuzzy relations increase the forecasting accuracy. In this study, a novel high order fuzzy time series forecasting approach in which multiplicative neuron model is used to define fuzzy relations is proposed in order to reach high forecasting level. Also, particle swarm optimization method is utilized to train multiplicative neuron model. In order to show forecasting performance of the proposed method, it is applied to a well-known data Taiwan future exchange and the results produced by the proposed approach is compared to those obtained from other fuzzy time series forecasting models. As a result of the implementation, it is observed that the proposed approach gives the best forecasts for Taiwan future exchange time series.