Prediction of TAIEX based on hybrid fuzzy time series model with single optimization process
| Autor: | Ozge Cagcag Yolcu, Faruk Alpaslan |
|---|---|
| Přispěvatelé: | Belirlenecek |
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
0209 industrial biotechnology
Series (mathematics) Artificial neural network Computer science Particle swarm optimization Fuzzy set 02 engineering and technology Fuzzy logic Defuzzification 020901 industrial engineering & automation Single multiplicative neuron model 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Time series Cluster analysis Algorithm Software Fuzzy time series Forecasting |
| Popis: | WOS: 000430162100002 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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