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)

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

Project	Size Score	Trend Score	Byline
PyCaret	8.5	8.0	An open-source, low-code machine learning library in Python.
PyGOD	2.0	6.75	A Python Library for Graph Outlier Detection (Anomaly Detection)
PyOD	2.75	8.75	A Comprehensive and Scalable Python Library for Outlier Detection (Anomaly Detection)
TODS	2.25	2.75	An Automated Time-series Outlier Detection System