Intuitionistic time series fuzzy inference system

Egrioglu, Erol and Bas, Eren and Yolcu, Ozge Cagcag and Yolcu, Ufuk (2019) Intuitionistic time series fuzzy inference system. Engineering Applications of Artificial Intelligence, 82. pp. 175-183. ISSN 0952-1976

Full text not available from this repository.


Although adaptive network fuzzy inference system and fuzzy functions approach can be utilized as a prediction tool, they have been not designed for prediction problem and they ignore the dependency structure of time series observations. From this viewpoint, making a design of the method that considers the dependency structure of observations will provide to get more accurate prediction. In this study, an intuitionistic time series fuzzy inference system (I-TSFIS) has been proposed. In the I-TSFIS, in just the same way as in the intuitionistic fuzzy inference systems, not only the membership values and crisp observations but also the non-membership values are used as inputs. Moreover, due to the use of crisp values as targets and outputs, the output does not need to be deffuzzified. Non-linear relationships between inputs and outputs of the proposed I-TSFIS are determined by Sigma-Pi neural network (SP-NN). The obtaining of optimal weights of SP-NN is performed by modified particle swarm optimization. And also I-TSFIS uses intuitionistic fuzzy C-means to obtain fuzzy clusters, membership and non-membership values of observations for these clusters. To evaluate the prediction performance of the proposed I-TSFIS, various real-life time series data sets have been analyzed and the results demonstrate the superior prediction ability of the proposed I-TSFIS.

Item Type:
Journal Article
Journal or Publication Title:
Engineering Applications of Artificial Intelligence
Uncontrolled Keywords:
ID Code:
Deposited By:
Deposited On:
27 Nov 2019 10:10
Last Modified:
27 Apr 2022 05:19