Practice✓ Mathematical
◆ The PatternFinding the signal in the noise
Temporal anomalies are observations that deviate from expected patterns. Point anomalies are individual outliers. Contextual anomalies are normal values at the wrong time (e.g., summer demand in winter). Methods range from simple z-score thresholds to autoencoders that learn the normal pattern.
// Interactive — anomaly detection methods
Threshold (σ)2.0
# Python — anomaly detection from sklearn.ensemble import IsolationForest # Z-score method z = (series - series.rolling(30).mean()) / series.rolling(30).std() anomalies = series[z.abs() > 2.5] # Isolation Forest on features clf = IsolationForest(contamination=0.05) labels = clf.fit_predict(features)
Pattern bridge: Anomaly detection in time series uses the same z-score logic from statistics. In markets, anomalies are price gaps and volume spikes. In MLOps, data quality gates perform anomaly detection on incoming features.