Akademik Veri Yönetim Sistemi
NEUROCOMPUTING, cilt.247, ss.87-101, 2017 (SCI-Expanded)
In case of outlier(s) it is inevitable that the performance of the fuzzy time series prediction methods is influenced adversely. Therefore, current prediction methods will not be able to provide satisfactory accuracy rates for defuzzified outputs (predictions) when the data has outlier(s). In this study, not only to be able to sort out this problem but also to be able to improve the forecasting accuracy, we propose a combined robust approach for fuzzy time series by assessing how the prediction performance of the methods will be affected from the outlier(s). In the proposed model, different from the current models, both crisp values and membership values are used as inputs and also real time series observations are taken as outputs. The proposed model therefore does not require defuzzification transaction and uses single multiplicative neuron model to determine the fuzzy relations and a robust fitness function in its training process. While performing the training process of this model by particle swarm optimization within a combined single optimization process, using crisps values and membership values together provides successful results by getting further information. The various implementations are illustrated to show that the proposed model could obtain more accurate and robust results in forecasting.an example is illustrated to show that the proposed method could obtain more accurate and robust results in forecasting.an example is illustrated to show that the proposed method could obtain more accurate and robust results in forecasting.an example is illustrated to show that the proposed method could obtain more accurate and robust results in forecasting. (C) 2017 Elsevier B.V. All rights reserved.