Smoothing parameter selection in Nadaraya-Watson kernel nonparametric regression using nature-inspired algorithm optimization

Abstract EN

In the context of Nadaraya-Watson kernel nonparametric regression, the curve estimation is fully depending on the smoothing parameter.

at this point, the nature-inspired algorithms can be used as an alternative tool to find the optimal selection.

In this paper, a firefly optimization algorithm method is proposed to choose the smoothing parameter in Nadaraya-Watson kernel nonparametric regression.

the proposed method will efficiently help to find the best smoothing parameter with a high prediction.

the proposed method is compared with four famous methods.

the experimental results comprehensively demonstrate the superiority of the proposed method in terms of prediction capability.