After my hardly working and searching, I could find some results:
Actually, there is no a benchmarking data set with uncertainties features. One solution is adding noise to the original data set to make uncertainties due to affection of the noise.
The ideal way is application of White Gaussian Noise
. Two ways are as follows:
(1) MATLAB can support this issue with the function wgn
.
(2) using randn
function from MATLAB.
(3) my suggestion is using Mean * randn(n,1) + Standard Deviation
, which add noise in your data set with your preferred mean
and Std. (Standard Deviation)
I hope that my recommendation being useful.