Anomaly Detection

Description

Anomaly detection (also called outlier detection) is about finding data points that do not fit into the overall pattern of a data set. Applications include failure detection, fraud detection, security, and data cleansing. Algorithms may work on arbitrary datasets or be specific to a data type, such as time series or graph data. In general, algorithms may look for individual outlier points, a pattern involving multiple points, or compare entire data sets. A related problem is change point detection, where one is looking for changes in the pattern of time series data.

Projects

4

Lines Committed vs. Age Chart (click to view)

Lines Committed vs. Age Chart (click to view)

Education Resources

URL

Resource Type

Description

https://github.com/yzhao062/anomaly-detection-resources

Example Code

Currated list of anomaly detection resources from Yue Zhao, creator of PyOD

https://youtu.be/I58aW_w1dwk?t=1280

Video

Talk by Linda Zhou on Time Series Anomaly Detection

Projects

Project

Size Score

Trend Score

Byline

PyCaret

7.25

6.0

An open-source, low-code machine learning library in Python.

PyGOD

1.75

8.75

A Python Library for Graph Outlier Detection (Anomaly Detection)

PyOD

2.25

7.5

A Comprehensive and Scalable Python Library for Outlier Detection (Anomaly Detection)

TODS

2.5

4.0

An Automated Time-series Outlier Detection System