anomaly-detection

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!

Merlion: A Machine Learning Framework for Time Series Intelligence

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 🚀.

Paper list and datasets for industrial image anomaly/defect detection (updating). 工业异常/瑕疵检测论文及数据集检索库(持续更新)。

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

A curated list of Graph/Transformer-based fraud, anomaly, and outlier detection papers & resources

Find big moving stocks before they move using machine learning and anomaly detection

AIOps学习资料汇总，欢迎一起补全这个仓库，欢迎star

MOMENT: A Family of Open Time-series Foundation Models, ICML'24