Prediction of TAIEX based on hybrid fuzzy time series model with single optimization process

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dc.authoridCAGCAG YOLCU, OZGE/0000-0003-3339-9313
dc.contributor.authorYolcu, Ozge Cagcag
dc.contributor.authorAlpaslan, Faruk
dc.date.accessioned2025-03-23T19:41:58Z
dc.date.available2025-03-23T19:41:58Z
dc.date.issued2018
dc.departmentSinop Üniversitesi
dc.description.abstractAll fuzzy time series approaches proposed in the literature consider three steps constituting the solution process as separate processes. Thus, model error is the sum of the errors that may occur in each step. In this regard, synchronous evaluation of the steps constituting the analysis process will produce a single model error and will lead to a reduction in the model error. Within the scope of this study, we proposed an approach which evaluates the steps constituting fuzzy time series analysis in one process synchronously to forecast the Taiwan Stock Exchange Capitalization Weighted Stock Index. In the proposed approach, defuzzification step is eliminated by using real values of time series as target values in the identification of fuzzy relations step. In this respect, determination of fuzzy cluster centres in fuzzification and the training of artificial neural network with single multiplicative neuron which is used the identification of fuzzy relation are carried out in a single optimization process with particle swarm optimization. This work also covers comprehensive literature review and summary info of related methodologies including fuzzy time series, particle swarm optimization, single multiplicative neuron model and fuzzy C-means clustering. The proposed method is applied to twelve different time series and a total of twenty-four measurements are done and superior forecasting performance of the method is proven. (C) 2018 Elsevier B.V. All rights reserved.
dc.identifier.doi10.1016/j.asoc.2018.02.007
dc.identifier.endpage33
dc.identifier.issn1568-4946
dc.identifier.issn1872-9681
dc.identifier.scopus2-s2.0-85042116807
dc.identifier.scopusqualityQ1
dc.identifier.startpage18
dc.identifier.urihttps://doi.org/10.1016/j.asoc.2018.02.007
dc.identifier.urihttps://hdl.handle.net/11486/6690
dc.identifier.volume66
dc.identifier.wosWOS:000430162100002
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofApplied Soft Computing
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20250323
dc.subjectFuzzy time series
dc.subjectForecasting
dc.subjectSingle multiplicative neuron model
dc.subjectParticle swarm optimization
dc.titlePrediction of TAIEX based on hybrid fuzzy time series model with single optimization process
dc.typeArticle

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