Fuzzy time series forecasting method based on Gustafson–Kessel fuzzy clustering

► A new fuzzy time series forecasting algorithm proposed. ► Proposed method used Gustafson-Kessel fuzzy clustering method in fuzzification stage. ► Proposed method was applied to enrollment data. ► Proposed method is outperformed many methods in the literature. Fuzzy time series approaches have bein...

Full description

Saved in:
Bibliographic Details
Published inExpert systems with applications Vol. 38; no. 8; pp. 10355 - 10357
Main Authors Egrioglu, E., Aladag, C.H., Yolcu, U., Uslu, V.R., Erilli, N.A.
Format Journal Article
LanguageEnglish
Published Elsevier Ltd 01.08.2011
Subjects
Clustering
Expert systems
Forecasting
Fuzzy
Fuzzy clustering
Fuzzy logic
Fuzzy set theory
Fuzzy time series
Time series
Forecasting
Fuzzy time series
Fuzzy clustering
Online AccessGet full text

Cover

More Information
Summary:► A new fuzzy time series forecasting algorithm proposed. ► Proposed method used Gustafson-Kessel fuzzy clustering method in fuzzification stage. ► Proposed method was applied to enrollment data. ► Proposed method is outperformed many methods in the literature. Fuzzy time series approaches have being increasingly attracted researchers’ attentions. The procedures on fuzzy time series actually consist of three stages; fuzzification, determination of fuzzy relations and defuzzification. Researches are generally concentrated on these stages and about improving them. In this study, we propose a new approach, which combines several techniques. In this approach, Gustafson–Kessel, which is a fuzzy clustering technique, is being used to fuzzification of time series. The proposed method is compared with the approaches in literature.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ObjectType-Article-2
ObjectType-Feature-1
ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2011.02.052