Outlier Detection Using Distribution Fitting in Univariate Datasets

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Outlier Detection Using Distribution Fitting in Univariate Datasets

Learn how to detect outliers using Probability Density Functions: Fast and lightweight models with explainable results.

Anomaly or novelty detection is applicable in a wide range of situations where a clear, early warning of an abnormal condition is required, such as for sensor data, security operations, and fraud detection, among others. Due to the nature of the problem, outliers do not frequently present themselves, and the lack of labels can make it difficult to create supervised models. Outliers are also often called anomalies or novelties, but between the two, there are some fundamental differences in the underlying assumptions and the modeling process. I will discuss the concepts of outlier detection and demonstrate the differences between anomalies and novelties. In the second part of the blog, I will demonstrate with hands-on examples how to create an unsupervised model for the detection of outliers using probability density fitting for univariate data sets. The distfit library is used across all examples.