Cleaning the univariate dataset with high noise
There are multiple algorithms for this. On one of the kaggle competitions I saw applying several algorithms and then throwing the data away by the number of votes from all algorithms. The algorithms that I remember: 


Remove all points above or below 2 standard deviations. X>mean+2*std dev, X &lt; mean-2*std dev. 2 is actually a parameter you need to optimize.
Remove the 5% of the highest and lowest values. X>95% quantile, X&lt;5% quantile. 5% is a parameter you need to optimize.
X - median > 2*1.4826*MAD (median absolute deviation. 1.4826*MAD is an estimator for standard deviation under some assumptions). And the same for lower bound. https://eurekastatistics.com/using-the-median-absolute-deviation-to-find-outliers/


Repeat several times until no points are thrown away.

Example of this method with voting from some other kaggle winners: https://nycdatascience.com/blog/student-works/kaggle-predict-consumer-credit-default/