Skip to content

Latest commit

 

History

History
31 lines (20 loc) · 2.54 KB

full.md

File metadata and controls

31 lines (20 loc) · 2.54 KB

Anomaly detection

Anomaly detection, also known as outlier detection, is a technique used to identify data points or patterns that deviate significantly from the norm or the expected behavior within a dataset. Anomalies are data points that are different from the majority of the data, and they could indicate interesting or potentially important events, errors, fraud, or defects.

Anomaly detection is used in various fields and applications, including fraud detection, network security, industrial quality control, medical diagnosis, and more. There are several methods for performing anomaly detection, each with its own strengths and weaknesses:

Statistical Methods:

  • Z-Score: Identifies anomalies based on how far a data point is from the mean in terms of standard deviations.
  • Modified Z-Score: Similar to the Z-Score, but uses the median and the median absolute deviation (MAD) for robustness against outliers.
  • IQR (Interquartile Range): Identifies anomalies based on data points that fall outside the range defined by the first and third quartiles of the data.

Distance-based Methods:

  • k-Nearest Neighbors (k-NN): Identifies anomalies as data points that have few neighbors within a specified distance.
  • DBSCAN (Density-Based Spatial Clustering of Applications with Noise): Identifies anomalies as points that do not belong to any cluster.

Model-Based Methods:

  • Autoencoders: Neural network models that learn to encode and decode data. Data points with high reconstruction error are considered anomalies.

  • Isolation Forest: Creates a random decision tree structure that isolates anomalies more effectively with fewer splits.

  • Density Estimation Methods:

  • Local Outlier Factor (LOF): Compares the density of a data point's neighbors to the density of its neighbors' neighbors. Anomalies have a lower density than their neighbors.

  • One-Class SVM: Learns a decision boundary around the normal data points and considers data points outside this boundary as anomalies.

  • Time Series Anomaly Detection:

  • Exponential Smoothing: Uses moving averages and exponential smoothing techniques to detect anomalies in time series data.

  • Prophet: A forecasting tool designed for time series analysis, which can identify anomalous patterns.

Choosing the appropriate anomaly detection method depends on the characteristics of your data, the type of anomalies you're looking for, and the trade-offs between false positives and false negatives. It's important to evaluate and fine-tune the method based on the specific application to achieve accurate and reliable anomaly detection results.