detect-anomalies-aiops

Apply machine learning to detect anomalies in operational metrics, correlate alerts, and reduce false positives.

Expected: Time series data loaded with regular intervals, missing values handled, features engineered for ML models.

On failure: If Prometheus connection fails, verify URL and network access, if data gaps exist use forward-fill or interpolation, ensure timestamp column is datetime type, check for memory issues with large date ranges (process in chunks).

使用时间序列分析（隔离森林、Prophet、LSTM）、警报关联和根本原因分析，对运营指标实施人工智能驱动的异常检测。通过智能识别系统指标、日志和跟踪中的真正异常来减少警报疲劳。当运营团队因警报量而不堪重负时、当检测到超出静态阈值的复杂多指标异常时、当季节性模式使阈值无效时，或者当需要在问题影响用户之前主动预测问题时，请使用。 来源：pjt222/development-guides。