Intuitionistic time series fuzzy inference system

Egrioglu E., Bas E., Yolcu Ö., Yolcu U.

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, vol.82, pp.175-183, 2019 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 82
  • Publication Date: 2019
  • Doi Number: 10.1016/j.engappai.2019.03.024
  • Journal Name: ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.175-183
  • Keywords: Fuzzy inference system, Intuitionistic fuzzy c-means, Sigma-pi neural network, Time series prediction, Particle swarm optimization, LOGICAL RELATIONSHIP GROUPS, NEURO-FUZZY, FORECASTING ENROLLMENTS, TEMPERATURE PREDICTION, HYBRID APPROACH, ANFIS MODELS, C-MEANS, ALGORITHM, OPTIMIZATION, INTERVALS
  • Marmara University Affiliated: No

Abstract

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.