An Improved Adaptive Time-Variant Model for Fuzzy-Time-Series Forecasting Enrollments based on Particle Swarm Optimization

In this paper an improved adaptive Time-Variant Model for fuzzy time series (ATVF) is proposed and this model try to predict the Alabama University enrollments well. This model acquires analysis window size (Time order) based on accuracy of forecasting in training phase and in testing phase heuristic rules help in the forecasting values and particle swarm optimization algorithm is uses for interval lengths improvement to acquire forecasting with better accuracy. The experiment results show that the proposed model achieves a significant improvement in forecasting accuracy as compared to other fuzzy-time-series models for forecasting enrollments of students of the University of Alabama.