Anomaly & Changepoint Detection
Residual-based detection, CUSUM, seasonal-hybrid methods, and the base-rate problem in alerting.
5concepts
58flashcards
35minutes of reading
- 01 CUSUM and Sequential Change Detection Why detecting a small persistent shift needs accumulated evidence rather than a threshold, how the cumulative sum statistic works, and the tradeoff between detection delay and false alarm rate.
- 02 Evaluating Anomaly Detectors Why precision and recall computed per timestamp mislead on ranges, the point-adjustment protocol that inflated a decade of results, and what an honest evaluation reports.
- 03 Seasonal-Hybrid Decomposition for Anomalies Why anomaly detection on a seasonal series must remove the seasonality first, how robust decomposition avoids letting anomalies distort the fit, and the residual tests that follow.
- 04 Unsupervised Anomaly Detection Methods The three families of detector that need no labels, what notion of "anomalous" each encodes, and why the choice is a modelling assumption rather than a performance question.