An anomaly detection library comprising state-of-the-art algorithms and features such as experiment management, hyper-parameter optimization, and edge inference.

Outlier detection data sets; Datasets; MREOD

Train, Evaluate, Optimize, Deploy Computer Vision Models via OpenVINO™

SegmentAE: A Python Library for Anomaly Detection Optimization

A python library for user-friendly forecasting and anomaly detection on time series.

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)。

PatchAD, deep learning, anomaly detection, outlier detection, time series, PatchAD: A Lightweight Patch-based MLP-Mixer for Time Series Anomaly Detection

Real Intelligence Threat Analytics (RITA) is a framework for detecting command and control communication through network traffic analysis.

The collection of pre-trained, state-of-the-art AI models for ailia SDK

autoupdate paper list

A Python package for an autoencoder-based algorithm to detect anomalies in distributed acoustic sensing (DAS) datasets.

Manage your detectors and identify atypical data in OpenSearch Dashboards

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 🚀.

ThirdEye is an integrated tool for realtime monitoring of time series and interactive root-cause analysis.

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.

Quadra: Effortless and reproducible deep learning workflows with configuration files.

This project presents a comprehensive evaluation of various unsupervised anomaly detection algorithms applied to datasets with mixed categorical and numerical attributes. The performance will be measured using a suite of metrics that assess clustering quality, anomaly detection precision, and computational efficiency.

Codebase for Unsupervised Anomaly Detection using Aggregated Normative Diffusion (ANDi)

Machine learning and data analysis package implemented in JavaScript and its online demo.

Android Application for Real-Time ECG Anomaly Detection Written in Kotlin