Anomaly detection in time series — what changes?
hardAnswer
- Temporal context matters.
- Options: (1) statistical: rolling mean/std + z-score, ARIMA residuals, STL decomposition + IQR on residuals.
- (2) Prophet / SARIMA residual monitoring.
- (3) LSTM-AE / Transformer-AE — reconstruction error on windows.
- (4) Anomaly Transformer / TimesNet.
- (5) Change-point detection (Bayesian, CUSUM).
- Consider: point anomalies, contextual anomalies, collective anomalies.
- Pipe with alerting + human review.
Check yourself — multiple choice
- Same as tabular
- Rolling stats / STL residuals / ARIMA / LSTM-AE / Anomaly Transformer / change-point; distinguish point / contextual / collective anomalies
- Random
- Only supervised
TS anomaly: statistical + AE + change-point; point/contextual/collective.
#anomaly-detection#applications
Practise Unsupervised Learning
214 interview questions in this topic.
Related questions
- How is drift detection an unsupervised problem?
- Covariate drift vs concept drift vs label drift — the differences.
- Scan statistics — when do you use them?
- CUSUM change-point detection — how does it work?
- How do you handle 99.9% normal / 0.1% anomaly training data?
- How do you explain why a point is anomalous?