Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
<mark>Journal publication date</mark> | 30/06/2019 |
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<mark>Journal</mark> | Engineering Applications of Artificial Intelligence |
Volume | 82 |
Number of pages | 9 |
Pages (from-to) | 175-183 |
Publication Status | Published |
Early online date | 12/04/19 |
<mark>Original language</mark> | English |
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.