There are 94 repositories under anomaly-detection topic.
Anomaly detection related books, papers, videos, and toolboxes
An open-source, low-code machine learning library in Python
A Comprehensive and Scalable Python Library for Outlier Detection (Anomaly Detection)
Merlion: A Machine Learning Framework for Time Series Intelligence
STUMPY is a powerful and scalable Python library for modern time series analysis
List of tools & datasets for anomaly detection on time-series data.
A curated list of awesome anomaly detection resources
Jupyter Notebook tutorials on solving real-world problems with Machine Learning & Deep Learning using PyTorch. Topics: Face detection with Detectron 2, Time Series anomaly detection with LSTM Autoencoders, Object Detection with YOLO v5, Build your first Neural Network, Time Series forecasting for Coronavirus daily cases, Sentiment Analysis with BERT.
A high-level machine learning and deep learning library for the PHP language.
Find big moving stocks before they move using machine learning and anomaly detection
Kibana Alert & Report App for Elasticsearch
Python programming assignments for Machine Learning by Prof. Andrew Ng in Coursera
Python codes in Machine Learning, NLP, Deep Learning and Reinforcement Learning with Keras and Theano
An anomaly detection library comprising state-of-the-art algorithms and features such as experiment management, hyper-parameter optimization, and edge inference.
Deep learning PyTorch library for time series forecasting, classification, and anomaly detection (originally for flood forecasting).
A log analysis toolkit for automated anomaly detection [ISSRE'16]
The collection of pre-trained, state-of-the-art AI models for ailia SDK
A toolkit for automated log parsing [ICSE'19, TDSC'18, ICWS'17, DSN'16]
RNN based Time-series Anomaly detector model implemented in Pytorch.
A large collection of system log datasets for log analysis research
An open-source framework for real-time anomaly detection using Python, ElasticSearch and Kibana
A Python toolkit for rule-based/unsupervised anomaly detection in time series
Machine Learning tutorials with TensorFlow 2 and Keras in Python (Jupyter notebooks included) - (LSTMs, Hyperameter tuning, Data preprocessing, Bias-variance tradeoff, Anomaly Detection, Autoencoders, Time Series Forecasting, Object Detection, Sentiment Analysis, Intent Recognition with BERT)
A framework for using LSTMs to detect anomalies in multivariate time series data. Includes spacecraft anomaly data and experiments from the Mars Science Laboratory and SMAP missions.
A collection of anomaly detection methods (iid/point-based, graph and time series) including active learning for anomaly detection/discovery, bayesian rule-mining, description for diversity/explanation/interpretability. Analysis of incorporating label feedback with ensemble and tree-based detectors. Includes adversarial attacks with Graph Convolutional Network.
GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training
Anomaly Detection on Dynamic (time-evolving) Graphs in Real-time and Streaming manner. Detecting intrusions (DoS and DDoS attacks), frauds, fake rating anomalies.
A machine learning library for detecting anomalies in signals.
A Python Library for Graph Outlier Detection (Anomaly Detection)
Luminaire is a python package that provides ML driven solutions for monitoring time series data.
WinDBG Anti-RootKit Extension
Code for Hands-on Unsupervised Learning Using Python (O'Reilly Media)
A PyTorch implementation of the Deep SVDD anomaly detection method
A Deep Graph-based Toolbox for Fraud Detection