A Python library for anomaly detection across tabular, time series, graph, text, and image data. 60+ detectors, benchmark-backed ADEngine orchestration, and an agentic workflow for AI agents.

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

TODS: An Automated Time-series Outlier Detection System

A toolkit for time series machine learning and deep learning

[TPAMI 2024] Awesome Resources of GNNs for Time Series Analysis (GNN4TS)

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

Awesome Easy-to-Use Deep Time Series Modeling based on PaddlePaddle, including comprehensive functionality modules like TSDataset, Analysis, Transform, Models, AutoTS, and Ensemble, etc., supporting versatile tasks like time series forecasting, representation learning, and anomaly detection, etc., featured with quick tracking of SOTA deep models.

ADRepository: Real-world anomaly detection datasets, including tabular data (categorical and numerical data), time series data, graph data, image data, and video data.

The official code 👩‍💻 for - TOTEM: TOkenized Time Series EMbeddings for General Time Series Analysis

Code for the paper "TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks"

Time series anomaly detection algorithm implementations for TimeEval (Docker-based)

Evaluation Tool for Anomaly Detection Algorithms on Time Series

CARLA: A self-supervised contrastive learning model for time series anomaly detection. Enhances anomaly detection by learning robust representations of time series data.

Awesome Time-Series and Spatio-Temporal Related

[official] PyTorch implementation of TimeVQVAE-AD, a time series anomaly detection model.

GutenTAG is an extensible tool to generate time series datasets with and without anomalies; integrated with TimeEval.

PyTorch implementation of "Drift doesn't Matter: Dynamic Decomposition with Dffusion Reconstruction for Unstable Multivariate Time Series Anomaly Detection" (NeurIPS 2023)

Supporting material and website for the paper "Anomaly Detection in Time Series: A Comprehensive Evaluation"

A simple-to-use Python tool for time series anomaly detection!

Domain Adaptation Contrastive learning model for Anomaly Detection in multivariate time series (DACAD), combining UDA with contrastive learning. DACAD utilizes an anomaly injection mechanism that enhances generalization across unseen anomalous classes, improving adaptability and robustness.