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Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/13572
Title: Compuational algorithm of fuzzy stochastic model for forecasting
Authors: Arumugam, Paramasivan
Senthamarai K.K.
Published in: Journal of Algorithms and Computational Technology
Abstract: In 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.
Citation: Journal of Algorithms and Computational Technology (2012), 6(3): 375-383
URI: https://doi.org/10.1260/1748-3018.6.3.375
http://repository.iitr.ac.in/handle/123456789/13572
Issue Date: 2012
Keywords: Computational Algorithm
Forecasting and Mean Square Error
Fuzzy logical relationship
Fuzzy time series
ISSN: 17483018
Author Scopus IDs: 16068034900
55339034900
Author Affiliations: Arumugam, P., Department of Statistics Manonmaniam, Sundaranar University, Tirunelveli, India
Senthamarai, K.K., Department of Statistics Manonmaniam, Sundaranar University, Tirunelveli, India
Corresponding Author: Arumugam, P.; Department of Statistics Manonmaniam, Sundaranar University, Tirunelveli, India; email: sixfacemsu@gmail.com
Appears in Collections:Journal Publications [PH]

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