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
Fuzzy time-series based on adaptive expectation model for TAIEX forecasting
Expert Systems with Applications: An International Journal
A bivariate fuzzy time series model to forecast the TAIEX
Expert Systems with Applications: An International Journal
A distance-based fuzzy time series model for exchange rates forecasting
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
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
Partitions based computational method for high-order fuzzy time series forecasting
Expert Systems with Applications: An International Journal
High-order fuzzy-neuro expert system for time series forecasting
Knowledge-Based Systems
An efficient time series forecasting model based on fuzzy time series
Engineering Applications of Artificial Intelligence
Hi-index | 12.05 |
Fuzzy time series models have been widely used to handle forecasting problems, such as forecasting enrollments, temperature, and the stock index. If we can get better forecasting accuracy rates, then we can get more benefits. In this paper, we present a new method to handle forecasting problems using high-order fuzzy logical relationships and automatic clustering techniques. The proposed method uses the proposed automatic clustering algorithm to partition the universe of discourse into different lengths of intervals. We also apply the proposed method to forecast the enrollments of the University of Alabama, the temperature and the TAIFEX. The experimental results show that the proposed method gets a higher average forecasting accuracy rate than the existing methods.