Anomaly detection in data science is the process of identifying rare items, events, or observations that deviate significantly from the majority of the data. You can explore community guides and technical breakdowns on Towards Data Science.
Core Types of Anomalies
• Point Anomalies: A single data instance that sits far outside the normal distribution (e.g., a massive fraudulent credit card charge).
• Contextual Anomalies: Data points that are abnormal only within a specific context or time frame (e.g., a high temperature reading in winter versus summer).
• Collective Anomalies: A collection of related data points that are not anomalous on their own, but become suspicious when they occur together sequentially.
Main Approaches & Techniques
• Statistical Methods: Use mathematical baselines like Z-scores, Interquartile Range (IQR), or Grubbs' test to flag values falling outside expected standard deviations (μ ± ασ).
• Unsupervised Machine Learning: Works on unlabeled data by assuming anomalies are rare and statistically different from normal points. Common models include:
• Isolation Forest: Randomly splits data to isolate outliers quickly; anomalies require fewer cuts.
• Local Outlier Factor (LOF): Measures the local density deviation of a data point compared to its neighbors.
• DBSCAN: A clustering-based approach that leaves sparse, disconnected points as noise/outliers.
• Supervised & Semi-Supervised Learning: Uses historical labeled datasets (e.g., Random Forests, k-NN, or Support Vector Machines) to classify normal versus anomalous behavior.
• Deep Learning: Employs Autoencoders or LSTMs to compress and reconstruct complex data; high reconstruction error signals an anomaly.
Common Use Cases
• Fraud Detection: Spotting irregular credit card transactions or login patterns.
• Cybersecurity: Catching unusual network traffic or unauthorized access attempts.
• Predictive Maintenance: Monitoring machine sensor telemetry to predict hardware failures before they happen.
• Healthcare: Identifying vital sign spikes or rare cellular patterns in diagnostics.
https://www.ibm.com/think/topics/anomaly-detection
https://www.ibm.com/think/topics/machine-learning-for-anomaly-detection
https://medium.com/data-science-collective/a-beginners-guide-to-anomaly-detection-cdebe88fc985
https://towardsdatascience.com/the-basics-of-anomaly-detection-65aff59949b7/
https://www.sciencedirect.com/science/article/pii/S2468227624003284
A Practical Toolkit for Time Series Anomaly Detection, Using Python
Here's how to detect point anomalies within each series, and identify anomalous signals across the whole bank
Piero Paialunga
December 17, 2025
https://towardsdatascience.com/a-practical-toolkit-for-time-series-anomaly-detection-using-python/
No comments:
Post a Comment