Issue of deciding interval length, calculations of complicated fuzzy logical relations and hunt of apposite defuzzification process have been an important area of research in fuzzy time series forecasting since its inception. In present study, cumulative probability distribution based computational scheme with discretized of universe is proposed for fuzzy time series forecasting. In this study, cumulative probability distribution decides the length of intervals using characteristic of data distribution and proposed computational algorithm minimizes calculations of complex fuzzy logical relations and search of suitable defuzzification method. To verify the enhancement in forecasting accuracy of developed model, it is applied to the benchmark problem of forecasting historical student enrollments of University of Alabama. Accuracy in forecasted enrollments of developed model is also compared with the other various methods using different error measures. Coefficients of correlation and determination are used to determine the strength between forecasted and actual enrollments.
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