Intuitionistic Fuzzy Time Series: An Approach for Handling Nondeterminism in Time Series Forecasting

Atanassov introduced the notion of intuitionistic fuzzy set as generalization of the fuzzy set, which has been proved to be very useful tool in handling nondeterminacy (hesitation) in the system. In the implementation of fuzzy time series forecasting, nondeterminacy is always neglected without any r...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IEEE transactions on fuzzy systems Jg. 24; H. 6; S. 1270 - 1281
Hauptverfasser: Kumar, Sanjay, Gangwar, Sukhdev Singh
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.12.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Data models
Economic forecasting
Forecasting
Fuzzy logic
Fuzzy sets
Hesitation
intuitionistic fuzzy logical relations (IFLRs)
intuitionistic fuzzy sets (IFSs)
intuitionistic fuzzy time series
Mathematical model
nondeterminacy
Predictive models
Time series
Time series analysis
Yttrium
ISSN:1063-6706, 1941-0034
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Atanassov introduced the notion of intuitionistic fuzzy set as generalization of the fuzzy set, which has been proved to be very useful tool in handling nondeterminacy (hesitation) in the system. In the implementation of fuzzy time series forecasting, nondeterminacy is always neglected without any reason. In this paper, we give the notion of intuitionistic fuzzy time series to handle the nondeterminism in time series forecasting. An intuitionistic fuzzy time series forecasting model is also proposed. The proposed intuitionistic fuzzy time series forecasting method uses intuitionistic fuzzy logical relations on time series data. Performance of the proposed method is verified by applying it on two time series data. The effectiveness of the proposed intuitionistic fuzzy time series forecasting method is verified by comparing the forecasted output with other intuitionistic-fuzzy-set-based fuzzy time series forecasting methods using root-mean-square error and average forecasting error.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1063-6706
1941-0034
DOI:10.1109/TFUZZ.2015.2507582