Anomaly & Changepoint Detection
Residual-based detection, CUSUM, seasonal-hybrid methods, and the base-rate problem in alerting.
7concepts
72flashcards
51minutes of reading
- 01 Bayesian Online Changepoint Detection How tracking a posterior over the time since the last change turns changepoint detection into exact, recursive Bayesian inference, what the run-length recursion computes at each step, and where its assumptions give way.
- 02 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.
- 03 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.
- 04 Multivariate Time Series Anomaly Detection Why anomalies in systems of many sensors often live in broken relationships rather than in any single channel, the classical and deep detectors built to find them, and why the benchmark evidence for the deep ones is much weaker than their reported scores suggest.
- 05 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.
- 06 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.