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Fuzzy time series models are of great interest in forecasting when the information is imprecise and vague. However, the major problem in fuzzy time series forecasting is the accuracy of the forecasted values. In the present study we propose a hybrid method of forecasting based on fuzzy time series and intuitionistic fuzzy sets. The proposed model is a simplified computational approach that uses the degree of nondeterminacy to establish fuzzy logical relations on time series data. The developed model was implemented on the historical enrollment data for the University of Alabama and the forecasted values were compared with the results of existing methods to show its superiority. The suitability of the proposed method was also examined in forecasting market share prices of the State Bank of India on the Bombay Stock Exchange, India.
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Keywords: degree of nondeterminacy; fuzzy logical relations; fuzzy time series; intuitionistic fuzzy sets; linguistic variables

Document Type: Research Article

Affiliations: Department of Mathematics, Statistics & Computer Science,G. B. Pant University of Agriculture & Technology, Pantnagar,Uttarakhand, India

Publication date: January 1, 2012

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