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
Handling forecasting problems using fuzzy time series
Fuzzy Sets and Systems
Forecasting enrollments using high-order fuzzy time series and genetic algorithms: Research Articles
International Journal of Intelligent Systems
Multivariate stochastic fuzzy forecasting models
Expert Systems with Applications: An International Journal
Fuzzy relation analysis in fuzzy time series model
Computers & Mathematics with Applications
Ratio-based lengths of intervals to improve fuzzy time series forecasting
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A Multivariate Heuristic Model for 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
Forecasting stock index price based on M-factors fuzzy time series and particle swarm optimization
International Journal of Approximate Reasoning
Two new time-variant methods for fuzzy time series forecasting
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Hi-index | 12.05 |
Many forecasting models based on the concepts of fuzzy time series have been proposed in the past decades. These models have been applied to predict enrollments, temperature, crop production and stock index, etc. In this paper, we present a simple heuristic time-invariant fuzzy time series forecasting model, which uses prediction accuracy of model observations to train the trend predictor in the training phase, and uses these trend predictor to generate forecasting values in the testing phase. This model can capture the trends of the time series more accurately and hence improve the forecasting results. The proposed method is applied for forecasting university enrollment of Alabama and the Taiwan Futures Exchange (TAIFEX). It is shown that the proposed model achieves a significant improvement in forecasting accuracy as compared to other fuzzy time series forecasting models.