Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/6019
Title: Type-1 fuzzy time series function method based on binary particle swarm optimisation
Authors: Aladag C. H.
Yolcu U.
Egrioğlu E.
Türkşen, İsmail Burhan
Keywords: Forecasting
Fuzzy functions
Fuzzy time series
Fuzzy time series function
Particle swarm optimisation
Issue Date: 2016
Publisher: Inderscience Enterprises Ltd.
Abstract: For time series forecasting four kinds of fuzzy-based approaches can be used. These are fuzzy regression techniques, fuzzy time series methods, fuzzy inference systems, and fuzzy function approaches. There are some major problems in using fuzzy regression techniques and fuzzy inference systems for time series forecasting. Therefore, it would be wise to use a forecasting approach which combines fuzzy time series and fuzzy function approaches. In this study, a fuzzy time series forecasting method based on fuzzy function approach is proposed by adopting fuzzy function approach to time series forecasting. And, the proposed approach is called type-1 fuzzy time series function approach. Also, in the proposed approach, the lagged variables of the system are determined by using binary particle swarm optimisation. In order to evaluate the performance of the proposed method, it has been applied to well-known time series of Australian beer consumption and Istanbul stock exchange dataset. © 2016 Inderscience Enterprises Ltd.
URI: https://doi.org/10.1504/IJDATS.2016.075970
https://hdl.handle.net/20.500.11851/6019
ISSN: 1755-8050
Appears in Collections:Endüstri Mühendisliği Bölümü / Department of Industrial Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

Show full item record

CORE Recommender

SCOPUSTM   
Citations

5
checked on Sep 23, 2022

Page view(s)

2
checked on Dec 26, 2022

Google ScholarTM

Check

Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.