Abstract
The main objective to design this proposed model is to overcome the drawbacks of the exiting approaches and derive more robust & accurate methodology to forecast data. This innovative soft computing time series model is designed by joint consideration of three key points (1) Event discretization of time series data (2 Frequency density based partitioning (3) Optimizing fuzzy relationship in inventive way. As with most of cited papers, historical enrollment of university of Alabama is used in this study to illustrate the new forecasting process. Subsequently, the performance of the proposed model is demonstrated by making comparison with some of the pre-existing forecasting methods. In general, the findings of the study are interesting and superior in terms of least Average Forecasting Error Rate (AFER) and Mean Square Error (MSE) values.
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