#Anomaly-detection
Showing 60 of 134 repositories tagged #anomaly-detection, ranked by stars
Cleanlab's open-source library is the standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.
A Python library for anomaly detection across tabular, time series, graph, text, image, and audio data. 60+ detectors, benchmark-backed ADEngine orchestration, and an agentic workflow for AI agents.
A unified framework for machine learning with time series
Open-source, low-code AutoML platform for Python. PyCaret 4.0: sklearn-native engine + React control plane.
A python library for user-friendly forecasting and anomaly detection on time series.
Anomaly detection related books, papers, videos, and toolboxes. Last update late 2025 for LLM and VLM works!
An anomaly detection library comprising state-of-the-art algorithms and features such as experiment management, hyper-parameter optimization, and edge inference.
Merlion: A Machine Learning Framework for Time Series Intelligence
STUMPY is a powerful and scalable Python library for modern time series analysis
TimeGPT-1: production ready pre-trained Time Series Foundation Model for forecasting and anomaly detection. Generative pretrained transformer for time series trained on over 100B data points. It's capable of accurately predicting various domains such as retail, electricity, finance, and IoT with just a few lines of code ๐.
Paper list and datasets for industrial image anomaly/defect detection (updating). ๅทฅไธๅผๅธธ/็็ตๆฃๆต่ฎบๆๅๆฐๆฎ้ๆฃ็ดขๅบ(ๆ็ปญๆดๆฐ)ใ
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 BER
The collection of pre-trained, state-of-the-art AI models for ailia SDK
Deep learning PyTorch library for time series forecasting, classification, and anomaly detection (originally for flood forecasting).
A high-level machine learning and deep learning library for the PHP language.
A Python toolkit/library for reality-centric machine/deep learning & data mining on partially-observed time series, with 50+ SOTA neural network models for scientific analysis tasks (imputation, classification, clustering, forecasting, anomaly detection, cleaning) on incomplete industrial irregularly-sampled multivariate TS with NaN missing values
Find big moving stocks before they move using machine learning and anomaly detection
A curated list of Graph/Transformer-based fraud, anomaly, and outlier detection papers & resources
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TODS: An Automated Time-series Outlier Detection System
A Python Library for Graph Outlier Detection (Anomaly Detection)
Python programming assignments for Machine Learning by Prof. Andrew Ng in Coursera
A machine learning toolkit for log-based anomaly detection [ISSRE'16]
Unsupervised time series anomaly detection library
Kibana Alert & Report App for Elasticsearch
RNN based Time-series Anomaly detector model implemented in Pytorch.
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)
Time-Series Work Summary in CS Top Conferences (NIPS, ICML, ICLR, KDD, AAAI, WWW, IJCAI, CIKM, ICDM, ICDE, etc.)
An open-source framework for real-time anomaly detection using Python, ElasticSearch and Kibana
A robust streaming log template miner based on the Drain algorithm
LogAI - An open-source library for log analytics and intelligence
A PyTorch implementation of the Deep SVDD anomaly detection method
ML powered analytics engine for outlier detection and root cause analysis.
A Deep Graph-based Toolbox for Fraud Detection
Code for Hands-on Unsupervised Learning Using Python (O'Reilly Media)
Resources for working with time series and sequence data
Python module for hyperspectral image processing
WinDBG Anti-RootKit Extension
Repository of the paper "A Systematic Evaluation of Deep Anomaly Detection Methods for Time Series".
OpenChatBI is an intelligent chat-based BI tool powered by large language models, designed to help users query, analyze, and visualize data through natural language conversations. It uses LangGraph and LangChain to build chat agent and workflows that support natural language to SQL conversion and data analysis.
iPython notebook and pre-trained model that shows how to build deep Autoencoder in Keras for Anomaly Detection in credit card transactions data
Deep Anomaly Detection with Outlier Exposure (ICLR 2019)
This repository contains a reading list of papers on Time Series Segmentation. This repository is still being continuously improved.
๐ฒ Implementation of the Robust Random Cut Forest algorithm for anomaly detection on streams
Timely detections for more proactive and effective actions in offshore oil wells!
A robust, and flexible open source User & Entity Behavior Analytics (UEBA) framework used for Security Analytics. Developed with luv by Data Scientists & Security Analysts from the Cyber Security Industry. [BETA]
Desbordante is a high-performance data profiler that is capable of discovering many different patterns in data using various algorithms. It also allows to run data cleaning scenarios using these algorithms. Desbordante has a console version and an easy-to-use web application.
A Python 3 library making time series data mining tasks, utilizing matrix profile algorithms, accessible to everyone.
Machine Learning Lectures at the European Space Agency (ESA) in 2018
A PyTorch implementation of Deep SAD, a deep Semi-supervised Anomaly Detection method.
Log Anomaly Detection - Machine learning to detect abnormal events logs
๐ฝ Out-of-Distribution Detection with PyTorch
Anomaly detection using LoOP: Local Outlier Probabilities, a local density based outlier detection method providing an outlier score in the range of [0,1].
A distributed Spark/Scala implementation of the isolation forest and extended isolation forest algorithms for unsupervised outlier detection, featuring support for scalable training and ONNX export for easy cross-platform inference.
A modular, skill-based autonomous Security Operations Center (SOC) agent that monitors OpenSearch/Elasticsearch data, builds RAG-based behavioral memory, and validates real-time anomalies using LLMs.
An End-to-End Benchmark Suite for Univariate Time-Series Anomaly Detection
Streaming Anomaly Detection Solution by using Pub/Sub, Dataflow, BQML & Cloud DLP
Interactive Visualization to Build, Train and Test an Autoencoder with Tensorflow.js