While working with any kind of data, we often encounter observations that are significantly different from the majority, that is, outliers. They can be a result of a wrong tick (price), something major happening on the financial markets, an error in the data processing pipeline, and so on. Many machine learning algorithms and statistical approaches can be influenced by outliers, leading to incorrect/biased results. That is why we should handle the outliers before creating any models.
In this recipe, we look into detecting outliers using the 3σ approach.
