Time series forecasting with interval type-2 intuitionistic fuzzy logic systems
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 uncertaint...
| Main Authors: | , , |
|---|---|
| Format: | Conference or Workshop Item |
| Published: |
IEEE
2017
|
| Online Access: | https://eprints.nottingham.ac.uk/41463/ |
| _version_ | 1848796279235149824 |
|---|---|
| author | Eyoh, Imo John, Robert de Maere, Geert |
| author_facet | Eyoh, Imo John, Robert de Maere, Geert |
| author_sort | Eyoh, Imo |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T19:45:27Z |
| format | Conference or Workshop Item |
| id | nottingham-41463 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:45:27Z |
| publishDate | 2017 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-414632020-05-04T19:01:50Z https://eprints.nottingham.ac.uk/41463/ Time series forecasting with interval type-2 intuitionistic fuzzy logic systems Eyoh, Imo John, Robert de Maere, Geert 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. IEEE 2017-08-24 Conference or Workshop Item PeerReviewed Eyoh, Imo, John, Robert and de Maere, Geert (2017) Time series forecasting with interval type-2 intuitionistic fuzzy logic systems. In: IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2017), 9-12 Jul 2017, Naples, Italy. http://ieeexplore.ieee.org/document/8015463/ |
| spellingShingle | Eyoh, Imo John, Robert de Maere, Geert Time series forecasting with interval type-2 intuitionistic fuzzy logic systems |
| title | Time series forecasting with interval type-2 intuitionistic fuzzy logic systems |
| title_full | Time series forecasting with interval type-2 intuitionistic fuzzy logic systems |
| title_fullStr | Time series forecasting with interval type-2 intuitionistic fuzzy logic systems |
| title_full_unstemmed | Time series forecasting with interval type-2 intuitionistic fuzzy logic systems |
| title_short | Time series forecasting with interval type-2 intuitionistic fuzzy logic systems |
| title_sort | time series forecasting with interval type-2 intuitionistic fuzzy logic systems |
| url | https://eprints.nottingham.ac.uk/41463/ https://eprints.nottingham.ac.uk/41463/ |