Anomaly Detection
Learn to detect anomalies in time-series and tabular data using various statistical, machine learning, and deep learning techniques. Implement methods like Z-score, Isolation Forest, Autoencoders, and Transformers for real-world applications.
4 Steps
- 1
Statistical Anomaly Detection: Z-Score: Calculate Z-scores for each data point in a time series. Points with Z-scores exceeding a threshold are flagged as anomalies.
- 2
Density-Based Anomaly Detection: DBSCAN: Apply DBSCAN to identify outliers as noise points in a dataset. Tune the `eps` and `min_samples` parameters for optimal performance.
- 3
Isolation Forest Anomaly Detection: Use Isolation Forest to isolate anomalies based on their lower density. Adjust the `n_estimators` and `contamination` parameters.
- 4
Autoencoder Anomaly Detection: Build an autoencoder to reconstruct normal data. Anomalies are identified by high reconstruction errors. This example uses a simple autoencoder with one hidden layer.
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