A computational method of forecasting based on fuzzy time series

Authors:
S. R. Singh
Affiliations:
Department of Mathematics, Banaras Hindu University, Varanasi 221005, India
Venue:
Mathematics and Computers in Simulation
Year:
2008

Citing 11
Cited 9

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Adaptive time-variant models for fuzzy-time-series forecasting

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A hybrid forecasting model for enrollments based on aggregated fuzzy time series and particle swarm optimization

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Abstract

In this paper, a computational method of forecasting based on fuzzy time series have been developed to provide improved forecasting results to cope up the situation containing higher uncertainty due to large fluctuations in consecutive year's values in the time series data and having no visualization of trend or periodicity. The proposed model is of order three and uses a time variant difference parameter on current state to forecast the next state. The developed model has been tested on the historical student enrollments, University of Alabama to have comparison with the existing methods and has been implemented for forecasting of a crop production system of lahi crop, containing higher uncertainty. The suitability of the developed model has been examined in comparison with the other models to show its superiority.