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Granular Computing
Erschienen in: Granular Computing 4/2019

29.11.2018 | Original Paper

Intuitionistic high-order fuzzy time series forecasting method based on pi-sigma artificial neural networks trained by artificial bee colony

verfasst von: Erol Egrioglu, Ufuk Yolcu, Eren Bas

Erschienen in: Granular Computing | Ausgabe 4/2019

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Abstract

Intuitionistic fuzzy sets are extended form of type 1 fuzzy sets. The modeling methods use intuitionistic fuzzy sets have second-order uncertainty approximation so these methods may have better results than methods based on type-1 fuzzy sets. Intuitionistic fuzzy sets have been used in forecasting methods and these methods are called intuitionistic fuzzy time series forecasting methods. In this study, new intuitionistic fuzzy time series definitions are made and a new forecasting method is proposed based on intuitionistic fuzzy sets. The first contribution of the paper is to make new fuzzy and intuitionistic fuzzy time series definitions. The second contribution is to make new forecasting model definitions for fuzzy and intuitionistic fuzzy time series. The last contribution is to propose a forecasting method for single-variable high-order intuitionistic fuzzy time series forecasting model. In the proposed method, fuzzification of observations is done by using intuitionistic fuzzy c-means algorithm and fuzzy relations are defined by pi-sigma artificial neural networks. Artificial bee colony algorithm is used to train Pi-Sigma artificial neural network in the proposed method. Real-world time series applications have been made for exploring performance of the proposed method.
Vorheriger Artikel Type 1 fuzzy function approach based on ridge regression for forecasting
Nächster Artikel Hesitant fuzzy set based computational method for financial time series forecasting

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Metadaten
Titel
Intuitionistic high-order fuzzy time series forecasting method based on pi-sigma artificial neural networks trained by artificial bee colony
verfasst von
Erol Egrioglu
Ufuk Yolcu
Eren Bas
Publikationsdatum
29.11.2018
Verlag
Springer International Publishing
Erschienen in
Granular Computing / Ausgabe 4/2019
Print ISSN: 2364-4966
Elektronische ISSN: 2364-4974
DOI
https://doi.org/10.1007/s41066-018-00143-5

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