AnomalyDetection <img class="emoji" alt="heart" src="https://cdn.jsdelivr.net/gh/qinwf/awesome-R@3c66da6e291bcc0520b1649125b0bed750896a9a/heart.png" height="20" align="absmiddle" width="20">

Uses the Seasonal Hybrid ESD algorithm to accurately identify both global and local anomalies even with seasonality and trends, as demonstrated in the README's examples of detecting subtle anomalies within seasonal bounds.

Supports both timestamped time series and simple numerical vectors via AnomalyDetectionTs and AnomalyDetectionVec functions, offering versatility when timestamps are unavailable, per the package's description.

Implements piecewise approximation to handle long time series (e.g., months of minutely data) without sacrificing accuracy, as mentioned for trend extraction in the presence of anomalies.

Provides annotated plots with customizable axes and anomaly highlighting, aiding visual analysis, as shown in the README's figures with clear anomaly annotations on time series data.