Deep learning for time series forecasting Flow forecast is an open-source deep learning for time series forecasting framework. It provides all the lat

ANEMONE A PyTorch implementation of "ANEMONE: Graph Anomaly Detection with Multi-Scale Contrastive Learning", CIKM-21 Dependencies python==3.6.1 dgl==

Generalized Out-of-Distribution Detection: A Survey 1. Overview This repository is with our survey paper: Title: Generalized Out-of-Distribution Detec

CutPaste CutPaste: image from paper Unofficial implementation of Google's "CutPaste: Self-Supervised Learning for Anomaly Detection and Localization"

taganomaly Anomaly detection labeling tool, specifically for multiple time series (one time series per category). Taganomaly is a tool for creating la

MemStream Implementation of MemStream: Memory-Based Anomaly Detection in Multi-Aspect Streams with Concept Drift . Siddharth Bhatia, Arjit Jain, Shivi

STUMPY STUMPY is a powerful and scalable library that efficiently computes something called the matrix profile, which can be used for a variety of tim

PyGOD is a Python library for graph outlier detection (anomaly detection). This exciting yet challenging field has many key applications, e.g., detect

Anomaly Detection Toolkit (ADTK) Anomaly Detection Toolkit (ADTK) is a Python package for unsupervised / rule-based time series anomaly detection. As

Curve Sorry to tell contributors and users. We decided to archive the project temporarily due to the employee work plan of collaborators. There are no

Time series analysis today is an important cornerstone of quantitative science in many disciplines, including natural and life sciences as well as eco

With CueObserve, you can run anomaly detection on data in your SQL data warehouses and databases. Getting Started Install via Docker docker run -p 300

IsoTree Fast and multi-threaded implementation of Extended Isolation Forest, Fair-Cut Forest, SCiForest (a.k.a. Split-Criterion iForest), and regular

LogDeep is an open source deeplearning-based log analysis toolkit for automated anomaly detection.

Outlier Exposure This repository contains the essential code for the paper Deep Anomaly Detection with Outlier Exposure (ICLR 2019). Requires Python 3

Who has never met a forged picture on the web ? No one ! Everyday we are constantly facing fake pictures touched up in Photoshop but it is not always

UNAVOIDS: Unsupervised and Nonparametric Approach for Visualizing Outliers and Invariant Detection Scoring Code Summary aggregate.py: this script aggr

Generalized Out-of-Distribution Detection: A Survey 1. Overview This repository is with our survey paper: Title: Generalized Out-of-Distribution Detec

🌲 Implementation of the Robust Random Cut Forest algorithm for anomaly detection on streams

Telemanom (v2.0) v2.0 updates: Vectorized operations via numpy Object-oriented restructure, improved organization Merge branches into single branch fo

luminol Overview Luminol is a light weight python library for time series data analysis. The two major functionalities it supports are anomaly detecti

Deep SAD: A Method for Deep Semi-Supervised Anomaly Detection This repository provides a PyTorch implementation of the Deep SAD method presented in ou

MatrixProfile MatrixProfile is a Python 3 library, brought to you by the Matrix Profile Foundation, for mining time series data. The Matrix Profile is

OOD-detection-using-OECC This repository contains the essential code for the paper Outlier Exposure with Confidence Control for Out-of-Distribution De

An Empirical Investigation of 3D Anomaly Detection and Segmentation Project | Paper Official PyTorch Implementation for the "An Empirical Investigatio

A library for benchmarking, developing and deploying deep learning anomaly detection algorithms Key Features • Getting Started • Docs • License Introd

Prototypical Networks for Few shot Learning in PyTorch Simple alternative Implementation of Prototypical Networks for Few Shot Learning (paper, code)

An open-source, low-code machine learning library in Python 🚀 Version 2.3.5 out now! Check out the release notes here. Official • Docs • Install • Tu

Anomaly Transformer in PyTorch This is an implementation of Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy. This pape

kafkaml-anomaly-detection Project for real time anomaly detection using kafka and python It's assumed that zookeeper and kafka are running in the loca

Industrial KNN-based Anomaly Detection ⭐ Now has streamlit support! ⭐ Run $ streamlit run streamlit_app.py This repo aims to reproduce the results of

An Unsupervised Graph-based Toolbox for Fraud Detection Introduction: UGFraud is an unsupervised graph-based fraud detection toolbox that integrates s

Merlion is a Python library for time series intelligence. It provides an end-to-end machine learning framework that includes loading and transforming data, building and training models, post-processi

Merlion: A Machine Learning Library for Time Series Table of Contents Introduction Installation Documentation Getting Started Anomaly Detection Foreca

Python Streaming Anomaly Detection (PySAD) PySAD is an open-source python framework for anomaly detection on streaming multivariate data. Documentatio

Surpriver - Find High Moving Stocks before they Move Find high moving stocks before they move using anomaly detection and machine learning. Surpriver

Banpei Banpei is a Python package of the anomaly detection. Anomaly detection is a technique used to identify unusual patterns that do not conform to

Anomaly Detection Learning Resources Outlier Detection (also known as Anomaly Detection) is an exciting yet challenging field, which aims to identify

(Py)TOD: Tensor-based Outlier Detection, A General GPU-Accelerated Framework Background: Outlier detection (OD) is a key data mining task for identify

A hands-off Anomaly Detection Library Table of contents What is Luminaire Quick Start Time Series Outlier Detection Workflow Anomaly Detection for Hig