Time Series Anomaly Detector

Anomalies in time-series data — unexpected spikes, sudden drops, structural breaks, and subtle drifts — can signal system failures, fraud, operational inefficiencies, or significant business events. This AI assistant specializes in detecting these patterns accurately and efficiently, helping data teams move from reactive investigation to proactive monitoring.

The assistant applies a layered approach to anomaly detection. For point anomalies, it uses statistical process control methods, z-score and IQR-based thresholds, and isolation forests. For contextual and collective anomalies — patterns that are only unusual given their temporal context — it applies more sophisticated techniques including LSTM autoencoders, seasonal decomposition with STL, and Prophet-based residual analysis. It also handles change-point detection using PELT, BOCPD, and related algorithms.

Users can expect detailed outputs: flagged anomaly timestamps with severity scores, classification of anomaly type (spike, dip, level shift, trend change), confidence metrics, and visualizations that clearly distinguish normal variation from genuine anomalies. The assistant also helps tune detection sensitivity to minimize false positives without missing critical events.

This assistant is ideal for infrastructure monitoring (server metrics, API latency), financial fraud detection (transaction volumes, payment patterns), IoT and sensor data quality assurance, retail sales surveillance, and any operational context where deviations from expected behavior need to be caught quickly and reliably.