Forecasting Enrollment Model Based on First-Order Fuzzy Time Series
Forecasting Enrollment Model Based on First-Order Fuzzy Time Series
This paper proposes a novel improvement of forecasting approach based on using time-invariant fuzzy time series. In contrast to traditional forecasting methods, fuzzy time series can be also applied to problems, in which historical data are linguistic values. It is shown that proposed time-invariant method improves the performance of forecasting process. Further, the effect of using different number of fuzzy sets is tested as well. As with the most of cited papers, historical enrollment of the University of Alabama is used in this study to illustrate the forecasting process. Subsequently, the performance of the proposed method is compared with existing fuzzy time series time-invariant models based on forecasting accuracy. It reveals a certain performance superiority of the proposed method over methods described in the literature.
Forecasting, fuzzy time series, linguistic values, student enrollment, time-invariant model
375-378
Sah, Melike
7483d16b-ef9a-4c24-9f00-0a01c630a844
Konstantin Y., Degtiarev
2e5ec3a6-7d70-4950-b6af-41fc95348326
1 January 2005
Sah, Melike
7483d16b-ef9a-4c24-9f00-0a01c630a844
Konstantin Y., Degtiarev
2e5ec3a6-7d70-4950-b6af-41fc95348326
Sah, Melike and Konstantin Y., Degtiarev
(2005)
Forecasting Enrollment Model Based on First-Order Fuzzy Time Series.
In World Academy of Science, Engineering and Technology, 1, .
Abstract
This paper proposes a novel improvement of forecasting approach based on using time-invariant fuzzy time series. In contrast to traditional forecasting methods, fuzzy time series can be also applied to problems, in which historical data are linguistic values. It is shown that proposed time-invariant method improves the performance of forecasting process. Further, the effect of using different number of fuzzy sets is tested as well. As with the most of cited papers, historical enrollment of the University of Alabama is used in this study to illustrate the forecasting process. Subsequently, the performance of the proposed method is compared with existing fuzzy time series time-invariant models based on forecasting accuracy. It reveals a certain performance superiority of the proposed method over methods described in the literature.
More information
Published date: 1 January 2005
Keywords:
Forecasting, fuzzy time series, linguistic values, student enrollment, time-invariant model
Organisations:
Electronics & Computer Science
Identifiers
Local EPrints ID: 265211
URI: http://eprints.soton.ac.uk/id/eprint/265211
ISSN: 1307-6884
PURE UUID: 7899eda2-51d0-4a69-ad9c-09ceb485f78a
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Date deposited: 27 Feb 2008 16:39
Last modified: 14 Mar 2024 08:05
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Contributors
Author:
Melike Sah
Author:
Degtiarev Konstantin Y.
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