A smoothing filter based on fuzzy transform

This paper is devoted to the smoothing of discrete functions using the fuzzy transform introduced by Perfilieva. We generalize a smoothing filter based on the fuzzy transform recently proposed by us to obtain a better control on the smoothed functions. For this purpose, a generalization of the concept of fuzzy partition is suggested and the smoothing filter is defined as a combination of the direct discrete fuzzy transform and a slightly modified inverse continuous fuzzy transform. An approximation behavior, total variation of smoothed functions and statistical properties including the description of the white noise reduction and the asymptotic expression of bias and variance are investigated and discussed. The proposed filter is compared with the Nadaraya–Watson estimator and the results are illustrated assuming financial data.