Anomaly Detection with StatsForecast and PyMC

Data is like garbage. You’d better know what you are going to do with it before you collect it.

– Mark Twain

In the diverse ecosystem of data analysis, one of the most intriguing yet complex areas you will encounter is anomaly detection. It’s a challenging task but crucial, as anomalies often signify critical events, such as potential fraud, system errors, or business trends that could impact decision-making.

Throughout this chapter, we’ll navigate through the intricacies of identifying these anomalies, honing our focus on how to handle different data types, including low-frequency data. Our exploration will range from understanding the fundamental nature of an anomaly to implementing a variety of techniques to detect anomalies. We will start with the concept of Seasonal–Trend decomposition using LOESS (STL) – a powerful technique that can help us discern anomalies from seasonal and trend...