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Outlier Detection
(also known as Anomaly Detection) is an exciting yet challenging field,
which aims to identify outlying objects that are deviant from the general data distribution.
Outlier detection has been proven critical in many fields, such as credit card
fraud analytics, network intrusion detection, and mechanical unit defect detection.
This repository collects:
Books & Academic Papers
Online Courses and Videos
Outlier Datasets
Open-source and Commercial Libraries/Toolkits
Key Conferences & Journals
More items will be added to the repository.
Please feel free to suggest other key resources by opening an issue report,
submitting a pull request, or dropping me an email @ ([email protected]).
Enjoy reading!
Outlier Analysis
by Charu Aggarwal: Classical text book covering most of the outlier analysis techniques.
A must-read for people in the field of outlier detection. [Preview.pdf]
Udemy Outlier Detection Algorithms in Data Mining and Data Science:
[See Video]
Stanford Data Mining for Cyber Security also covers part of anomaly detection techniques:
[See Video]
3. Toolbox & Datasets
3.1. Multivariate Data
[Python] Python Outlier Detection (PyOD): PyOD is a comprehensive and scalable Python toolkit for detecting outlying objects in multivariate data. It contains more than 20 detection algorithms, including emerging deep learning models and outlier ensembles.
[Python] Python Streaming Anomaly Detection (PySAD): PySAD is a streaming anomaly detection framework in Python, which provides a complete set of tools for anomaly detection experiments. It currently contains more than 15 online anomaly detection algorithms and 2 different methods to integrate PyOD detectors to the streaming setting.
[Python] Scalable Unsupervised Outlier Detection (SUOD): SUOD (Scalable Unsupervised Outlier Detection) is an acceleration framework for large-scale unsupervised outlier detector training and prediction, on top of PyOD.
[Julia] OutlierDetection.jl: OutlierDetection.jl is a Julia toolkit for detecting outlying objects, also known as anomalies.
[Java] RapidMiner Anomaly Detection Extension: The Anomaly Detection Extension for RapidMiner comprises the most well know unsupervised anomaly detection algorithms, assigning individual anomaly scores to data rows of example sets. It allows you to find data, which is significantly different from the normal, without the need for the data being labeled.
[Python] TODS: TODS is a full-stack automated machine learning system for outlier detection on multivariate time-series data.
[Python] skyline: Skyline is a near real time anomaly detection system.
[Python] banpei: Banpei is a Python package of the anomaly detection.
[Python] telemanom: A framework for using LSTMs to detect anomalies in multivariate time series data.
[Python] DeepADoTS: A benchmarking pipeline for anomaly detection on time series data for multiple state-of-the-art deep learning methods.
[Python] NAB: The Numenta Anomaly Benchmark: NAB is a novel benchmark for evaluating algorithms for anomaly detection in streaming, real-time applications.
[Python] CueObserve: Anomaly detection on SQL data warehouses and databases.
[Python] Chaos Genius: ML powered analytics engine for outlier/anomaly detection and root cause analysis.
[R] AnomalyDetection: AnomalyDetection is an open-source R package to detect anomalies which is robust, from a statistical standpoint, in the presence of seasonality and an underlying trend.
[R] anomalize: The 'anomalize' package enables a "tidy" workflow for detecting anomalies in data.
3.3. Graph Outlier Detection
[Python] Python Graph Outlier Detection (PyGOD): PyGOD is a Python library for graph outlier detection (anomaly detection). It includes more than 10 latest graph-based detection algorithms
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Ahmed, M., Mahmood, A.N. and Islam, M.R., 2016. A survey of anomaly detection techniques in financial domain. Future Generation Computer Systems, 55, pp.278-288.
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Campos, G.O., Moreira, E., Meira Jr, W. and Zimek, A., 2019. Outlier Detection in Graphs: A Study on the Impact of Multiple Graph Models. Computer Science & Information Systems, 16(2).
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