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dc.contributor.authorArumugam, Paramasivan-
dc.contributor.authorSenthamarai K.K.-
dc.identifier.citationJournal of Algorithms and Computational Technology (2012), 6(3): 375-383-
dc.description.abstractIn our daily life, often we use forecasting techniques to predict weather, economy, population growth, stock, etc. In recent years, many fuzzy time series methods are developed for forecasting of enrollments of Universities. Song and Chissom (1993) were the pioneers in studying such type of problems. Shiva Raj Sing (2007) presented a simple time variant method for forecasting the enrollment of the University of Alabama using fuzzy time series. Forecasts are needed only if there is uncertainty about the future. In this paper develop the modified algorithm to forecast enrollment for the same data set and compared with existing methods. The proposed method shows the better forecasting accuracy rate for enrollments, compared than other methods.-
dc.relation.ispartofJournal of Algorithms and Computational Technology-
dc.subjectComputational Algorithm-
dc.subjectForecasting and Mean Square Error-
dc.subjectFuzzy logical relationship-
dc.subjectFuzzy time series-
dc.titleCompuational algorithm of fuzzy stochastic model for forecasting-
dc.affiliationArumugam, P., Department of Statistics Manonmaniam, Sundaranar University, Tirunelveli, India-
dc.affiliationSenthamarai, K.K., Department of Statistics Manonmaniam, Sundaranar University, Tirunelveli, India-
dc.description.correspondingauthorArumugam, P.; Department of Statistics Manonmaniam, Sundaranar University, Tirunelveli, India; email:
Appears in Collections:Journal Publications [PH]

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