An Intuitionistic Fuzzy Time Series Model Based on New Data Transformation Method

Authors

Long-Sheng Chen^1, , Mu-Yen Chen², Jing-Rong Chang^{3, *,} , Pei-Yu Yu⁴

¹Professor, Department of Information Management, Chaoyang University of Technology, 168, Jifeng E. Rd., Wufeng District, Taichung, 41349, Taiwan

²Associate Professor, Department of Engineering Science, National Cheng Kung University, No. 1, University Road, Tainan, 701, Taiwan

³Assistant Professor, Department of Information Management, Chaoyang University of Technology, 168, Jifeng E. Rd., Wufeng District, Taichung, 41349, Taiwan

⁴Master, Department of Information Management, Chaoyang University of Technology, 168, Jifeng E. Rd., Wufeng District, Taichung, 41349, Taiwan

^*Corresponding author. Email: chrischang@cyut.edu.tw

DOI

10.2991/ijcis.d.210106.002How to use a DOI?

Keywords

Intuitionistic fuzzy sets (IFS); Intuitionistic fuzzy time series (IFTS); Nth quantile discretization approach (NQDA); Forecasting

Abstract

Traditional time series methods can predict seasonal problems, but not problems with transferred linguistic data. Thus, a forecasting method for such problems is required. However, existing intuitionistic fuzzy time series forecasting methods lack persuasiveness in determining the degree of hesitation and the lengths of intervals. Hence, this research is mainly to explore how to decide the degree of hesitation for each interval for intuitionistic fuzzy time series. This paper proposes the weighted intuitionistic fuzzy time series model based on the Nth quantile discretization approach (NQDA). The proposed model can decide the appropriate number, interval length, degree of hesitation, and membership and nonmembership functions of linguistic values on the basis of the training data. In the experimental section, the forecasts of several data sets are made for model validation. Results indicate that the proposed model can be used to obtain forecasts for other time-related data sets.

Copyright

© 2021 The Authors. Published by Atlantis Press B.V.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Long-Sheng Chen
AU  - Mu-Yen Chen
AU  - Jing-Rong Chang
AU  - Pei-Yu Yu
PY  - 2021
DA  - 2021/01/12
TI  - An Intuitionistic Fuzzy Time Series Model Based on New Data Transformation Method
JO  - International Journal of Computational Intelligence Systems
SP  - 550
EP  - 559
VL  - 14
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.210106.002
DO  - 10.2991/ijcis.d.210106.002
ID  - Chen2021
ER  -