How to detect anomalies in metrics?

There are several approaches to detecting anomalies in metrics:

• Statistical analysis: identifying values that fall outside the expected range (for example, using standard deviation).
• Thresholds: setting upper and lower bounds for metrics, triggering an alert when exceeded.
• Sliding windows and trends: analyzing changes in metrics over time to detect sharp jumps or drops.
• Machine learning: using algorithms, such as clustering or forecasting models, to identify unusual patterns.
• Visualization: graphs and dashboards help quickly spot deviations.

It is also important to consider context and seasonality to avoid interpreting normal fluctuations as anomalies.