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
Information Sciences: an International Journal
Forecasting enrollments using high-order fuzzy time series and genetic algorithms: Research Articles
International Journal of Intelligent Systems
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Multi-attribute fuzzy time series method based on fuzzy clustering
Expert Systems with Applications: An International Journal
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
Handling forecasting problems based on two-factors high-order fuzzy time series
IEEE Transactions on Fuzzy Systems
A multi-agent system to assist with property valuation using heterogeneous ensembles of fuzzy models
KES-AMSTA'10 Proceedings of the 4th KES international conference on Agent and multi-agent systems: technologies and applications, Part I
ACS'10 Proceedings of the 10th WSEAS international conference on Applied computer science
Handling forecasting problems based on high-order fuzzy logical relationships
Expert Systems with Applications: An International Journal
Information Sciences: an International Journal
Hi-index | 12.05 |
In recent years, some researchers focused on the research topic of using fuzzy time series to handle forecasting problems. In this paper, we present a new method to forecast enrollments based on automatic clustering techniques and fuzzy logical relationships. First, we present an automatic clustering algorithm for clustering historical enrollments into intervals of different lengths. Then, each obtained interval will be divided into p sub-intervals, where p=1. Based on the new obtained intervals and fuzzy logical relationships, we present a new method for forecasting the enrollments of the University of Alabama. The proposed method gets a higher average forecasting accuracy rate than the existing methods.