The fuzzy transform (F-transform), introduced by I. Perfilieva, is a powerful tool for the construction of fuzzy approximation models; it is based on generalized fuzzy partitions and it is obtained by minimizing a quadratic (L2-norm) error function. In this paper, within the discrete setting, we describe an analogous construction by minimizing an L1-norm error function, so obtaining the L1-norm F-transform, which is again a general approximation tool. The L1-norm and L2-norm settings are then used to construct two types of fuzzy- valued F-transforms, by defining expectile (L2-norm) and quantile (L1-norm) extensions of the transforms. This allows to model an observed time series in terms of fuzzy-valued functions, whose level-cuts can be interpreted in the setting of expectile and quantile regression. The proposed methodology is illustrated on some financial daily time series.
Quantile and expectile smoothing based on L1-norm and L2-norm fuzzy transforms / Guerra, Maria Letizia; Sorini, Laerte; Stefanini, Luciano. - In: INTERNATIONAL JOURNAL OF APPROXIMATE REASONING. - ISSN 0888-613X. - STAMPA. - 107:(2019), pp. 17-43. [10.1016/j.ijar.2019.01.011]
Quantile and expectile smoothing based on L1-norm and L2-norm fuzzy transforms
Guerra, Maria Letizia
;
2019
Abstract
The fuzzy transform (F-transform), introduced by I. Perfilieva, is a powerful tool for the construction of fuzzy approximation models; it is based on generalized fuzzy partitions and it is obtained by minimizing a quadratic (L2-norm) error function. In this paper, within the discrete setting, we describe an analogous construction by minimizing an L1-norm error function, so obtaining the L1-norm F-transform, which is again a general approximation tool. The L1-norm and L2-norm settings are then used to construct two types of fuzzy- valued F-transforms, by defining expectile (L2-norm) and quantile (L1-norm) extensions of the transforms. This allows to model an observed time series in terms of fuzzy-valued functions, whose level-cuts can be interpreted in the setting of expectile and quantile regression. The proposed methodology is illustrated on some financial daily time series.File | Dimensione | Formato | |
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IJA_GueSorSte2019.pdf
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