Time series forecasting with interval type-2 intuitionistic fuzzy logic systemsTools 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. Full text not available from this repository.
Official URL: http://ieeexplore.ieee.org/document/8015463/
AbstractConventional 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.
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