Statistical
Recurring Pattern Detection
Updated May 31, 2026 · today
Identifies log patterns that recur on a schedule — daily batch jobs, hourly cron runs, weekly reports — and flags when one fails to fire on its expected cadence.
Example alert
Exact wording varies — the detector generates titles from the anomaly it finds. This is representative of what an alert looks like when it fires.
How it works
Detects periodicity in log pattern timestamps using autocorrelation. Once a recurring schedule is learned (daily, hourly, weekly), a missed occurrence triggers an alert. Learning period: 2–3 full cycles of the detected cadence.
Availability
Runs on these tiers:
Want to see this detector firing in the live demo?
Open alerts in the sandbox →Related detectors
- Volume Anomaly
Detects spikes, drops, and flatlines in log volume vs daily and weekly baselines per service.
- Silence Detection
Catches services that stop logging when they normally log every N seconds. The most dangerous failure mode: no errors, just absence.
- Outlier Detection
Multi-dimensional outliers in log feature space. Catches subtle anomalies that single-axis thresholds miss.
- Error Rate Anomaly
Per-service error percentage anomalies vs baseline, with sustained-elevation guards so a single noisy minute doesn't fire and slow ramps still get caught.