Summary

This chapter delved into anomaly detection, exploring various strategies to effectively pinpoint unexpected patterns or outliers within data, which often indicate critical events or atypical activities that merit further investigation. It began with the STL method, which breaks down a time series into trend, seasonality, and remainder components, thereby facilitating the identification of anomalies that cannot be accounted for by underlying trends or seasonality. We then shifted to the robust S-H-ESD test, known for its effectiveness in identifying anomalies in time-series data, and the use of forecasting tools such as the StatsForecast library for anomaly detection, which, despite certain limitations with low-frequency data, has proven adaptable and beneficial across diverse data scenarios.

The chapter wrapped up by weighing the pros and cons of each technique discussed, emphasizing the importance of choosing the right method based on the specific nature of the data and...