Prediction of TAIEX based on hybrid fuzzy time series model with single optimization process
[ X ]
Tarih
2018
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
All 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.
Açıklama
Anahtar Kelimeler
Fuzzy time series, Forecasting, Single multiplicative neuron model, Particle swarm optimization
Kaynak
Applied Soft Computing
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
66
