Intuitionistic time series fuzzy inference system. (June 2019) - Egrioglu, Erol; Bas, Eren; Yolcu, Ozge Cagcag; Yolcu, Ufuk
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. Highlights: An IS with dependency structure of observations can ensure successful prediction. We have proposed an intuitionistic time series fuzzy inference system (I-TSFIS). I-TSFIS, as inputs, uses the membership, the non-membership and the crisp values. I-TSFIS forms non-linear relationships between inputs and outputs via SP-NN. The training of SP-NN is performed by modified particle swarm optimization.