Time series forecasting with interval type-2 intuitionistic fuzzy logic systems

Eyoh, Imo; John, Robert; de Maere, Geert

Abstract

Conventional fuzzy time series approaches make use of type-1 or type-2 fuzzy models. Type-1 models with one index (membership grade) cannot fully handle the level of uncertainty inherent in many real world applications. The type-2 models with upper and lower membership functions do handle uncertainties in many applications better than its type-1 counterparts. This study proposes the use of interval type-2 intuitionistic fuzzy logic system of Takagi-Sugeno-Kang (IT2IFLS-TSK) fuzzy inference that utilises more parameters than type-2 fuzzy models in time series forecasting. The IT2IFLS utilises more indexes namely upper and lower non-membership functions. These additional parameters of IT2IFLS serve to refine the fuzzy relationships obtained from type-2 fuzzy models and ultimately improve the forecasting performance. Evaluation is made on the proposed system using three real world benchmark time series problems namely: Santa Fe, tree ring and Canadian lynx datasets. The empirical analyses show improvements of prediction of IT2IFLS over other approaches on these datasets.

• Conference Name: IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2017)
• End Date: Jul 12, 2017
• Acceptance Date: Mar 17, 2017
• Online Publication Date: Aug 24, 2017
• Deposit Date: Mar 22, 2017
• Publicly Available Date: Aug 24, 2017
• Journal: Proceedings of the IEEE International Fuzzy Systems Conference
• Electronic ISSN: 1544-5615
• Peer Reviewed: Peer Reviewed
• Public URL: https://nottingham-repository.worktribe.com/output/878750
• Publisher URL: http://ieeexplore.ieee.org/document/8015463/
• Related Public URLs: http://www.fuzzieee2017.org/
• Additional Information: 1544-5615. © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.