In the "coding path" of Python data processing, there are always some repetitive ... and filtering high-frequency outliers in data... All of these revolve around the fundamental process of "counting".

While the science behind identifying outliers is quite complex, the performance and accuracy of running algorithms over a data set is important for fraud detection. Fraud detection is a great example ...

In other words, the standard approach for identifying outliers via IQR isn’t practical. Fortunately, there are other techniques that are better-suited to finding outliers in financial markets.

An approach to detecting outliers in a linear model is developed. An outlier is defined to be an observation with a large random error, generated by the linear model under consideration. Outliers are ...