#anomaly-detection

Implement anomaly detection systems for fraud detection, network security, and quality control applications. Statistical methods: 1. Z-score analysis: standard deviation-based detection, threshold ±3 for outliers. 2. Interquartile Range (IQR): Q3 + 1.5*IQR upper bound, Q1 - 1.5*IQR lower bound. 3. Modified Z-score: median-based, robust to outliers, threshold ±3.5. Machine learning approaches: 1. Isolation Forest: tree-based isolation, anomaly score calculation, contamination parameter tuning. 2. One-Class SVM: unsupervised learning, normal behavior boundary, nu parameter optimization. 3. Local Outlier Factor (LOF): density-based detection, local density comparison, k-nearest neighbors. Deep learning methods: 1. Autoencoders: reconstruction error-based detection, bottleneck representation, threshold tuning. 2. Variational Autoencoders (VAE): probabilistic approach, reconstruction probability, latent space analysis. 3. LSTM autoencoders: sequential data anomalies, time series patterns, prediction error analysis. Time series anomaly detection: 1. Prophet: trend and seasonality decomposition, confidence intervals, changepoint detection. 2. Seasonal decomposition: residual analysis, seasonal pattern deviations. 3. Moving averages: deviation from expected patterns, adaptive thresholds. Evaluation metrics: 1. Precision: true anomalies / detected anomalies, minimize false alarms. 2. Recall: detected anomalies / total anomalies, maximize anomaly capture. 3. F1-score: balanced precision and recall, compare different methods. Real-time detection: streaming data processing, concept drift adaptation, online learning algorithms, alert systems with severity levels, investigation workflows for detected anomalies.

Build an anomaly detection system for transaction fraud. Approach: 1. Use Isolation Forest for unsupervised outlier detection. 2. Engineer features (transaction amount, time of day, location distance). 3. Set contamination parameter based on historical fraud rate. 4. Generate anomaly scores and flag top 1% as suspicious. 5. Create alerting system with precision/recall monitoring. Visualize anomalies on a scatter plot with decision boundary. Balance false positives vs fraud detection.