<abstract xmlns="http://www.w3.org/1999/xhtml"><p>This research article suggests a computational method for constructing fuzzy sets in absence of expert knowledge. This method uses concepts of central tendencies mean and variance. This study gives a solution to the critical issue in designing of fuzzy systems, number of fuzzy sets. Proposed computational method helps in finding intervals and thereby fuzzy sets for fuzzy time series forecasting. Proposed computational method is implemented on the authentic data for the enrolments of University of Alabama, which is considered as benchmark problem in the field of fuzzy time series. The forecasted values are compared with the results of other methods to state its supremacy. Projected computational method along with Gaussian membership function gave promising results over other methods for fuzzy time series for the above said benchmark data.</p></abstract>

This research article suggests a computational method for constructing fuzzy sets in absence of expert knowledge. This method uses concepts of central tendencies mean and variance. This study gives a solution to the critical issue in designing of fuzzy systems, number of fuzzy sets. Proposed computational method helps in finding intervals and thereby fuzzy sets for fuzzy time series forecasting. Proposed computational method is implemented on the authentic data for the enrolments of University of Alabama, which is considered as benchmark problem in the field of fuzzy time series. The forecasted values are compared with the results of other methods to state its supremacy. Projected computational method along with Gaussian membership function gave promising results over other methods for fuzzy time series for the above said benchmark data.

A Unique Computational Method for Constructing Intervals in Fuzzy Time Series Forecasting

Department of Mathematics, Govt. P.G. College

Department of Mathematics, Jaypee Institute of Information Technology

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

This research article suggests a computational method for constructing fuzzy sets in absence of expert knowledge. This method uses concepts of central...

A Unique Computational Method for Constructing Intervals in Fuzzy Time Series Forecasting

Online veröffentlicht: 30. März 2018

Seitenbereich: 3 - 10

Eingereicht: 17. Okt. 2017

Akzeptiert: 20. Nov. 2017

DOI: https://doi.org/10.2478/cait-2018-0001

Schlüsselwörter
Fuzzy logic, central tendencies, membership function, prediction, fuzzy time series

© 2018 Shilpa Jain et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

A Unique Computational Method for Constructing Intervals in Fuzzy Time Series Forecasting

Online veröffentlicht: 30. März 2018

Seitenbereich: 3 - 10

Eingereicht: 17. Okt. 2017

Akzeptiert: 20. Nov. 2017

DOI: https://doi.org/10.2478/cait-2018-0001

SchlüsselwörterFuzzy logic, central tendencies, membership function, prediction, fuzzy time series

© 2018 Shilpa Jain et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
Fuzzy logic, central tendencies, membership function, prediction, fuzzy time series