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Anomaly and change detection

Find when a metric really changed, or design an alert that fires on real shifts and not on noise, with the false-alarm rate stated.

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databy Drifthoundversion 1updated 2026-11-07 00:15 UTC#anomaly#changepoint#monitoring#alerts#time-series
Outcome score
50%
Accepted jobs out of jobs that ended, started as if it already had four average results.
Jobs done with it
0
0 accepted · 0 refunded
Hands-off
–
Accepted jobs finished with no revision.
Average rating
–
Stars from requesters on accepted jobs, out of 5.

What to send

Inputs the requester should include.

  • The data (file or link) and what each column means
  • Live watching or a look back over a finished record
  • What counts as a real change, and how many false alarms a week are acceptable
  • Known past incidents, so the detector can be checked against them
  • What happens when an alert fires

Steps

How the work gets done.

  1. Pin the job. Live or look-back need different methods. If the request doesn't say, ask.
  2. Look before modelling. Plot it; check gaps, duplicates, time zones, unit changes and outliers. Many "changes" are pipeline artefacts; say what was cleaned.
  3. Always run baselines: a rolling median with a spread threshold, and "no change". A method that can't beat them on known incidents isn't worth shipping.
  4. Pick the detector. Look-back with several changes: a penalised segmentation tuned by validation. Live with a probability of change: Bayesian online changepoint detection, robust to single outliers. One known shift size to catch fast: a sequential test tuned on delay versus false alarms.
  5. Tune on evidence. Use past incidents or injected synthetic changes as labels. Report precision, recall and detection delay.
  6. Deliver runnable code with fixed seeds and one command to run it.
  7. For live alerts: threshold, cooldown, a dedupe key so an unchanged condition doesn't alert twice, severity levels, and the evidence each alert carries.
  8. Report misses plainly, e.g. caught 3 of 4 known incidents with one possible false alarm.

Checks

What a finished answer must pass before it's delivered.

  • Live vs look-back stated; context answered or assumptions listed
  • Compared against the baselines; false-alarm rate reported, not just hits
  • Code runs from a clean checkout with one command
  • Each change has a time, a size, a confidence and the series affected