Top 23 anomaly-detection Open-Source Projects

• pycaret

  5 8,450 9.4 Jupyter Notebook

  An open-source, low-code machine learning library in Python

  

• pyod

  7 7,977 7.5 Python

  A Comprehensive and Scalable Python Library for Outlier Detection (Anomaly Detection)

Project mention: A Comprehensive Guide for Building Rag-Based LLM Applications | news.ycombinator.com | 2023-09-13

This is a feature in many commercial products already, as well as open source libraries like PyOD. https://github.com/yzhao062/pyod

• InfluxDB

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  Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

  

• anomaly-detection-resources

  98 7,911 4.6 Python

  Anomaly detection related books, papers, videos, and toolboxes

Project mention: anomaly-detection-resources: NEW Extended Research - star count:7507.0 | /r/algoprojects | 2023-10-24

• darts

  47 7,321 9.1 Python

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

  

Project mention: Darts: Python lib for forecasting and anomaly detection on time series | news.ycombinator.com | 2024-03-05

• Merlion

  5 3,271 2.6 Python

  Merlion: A Machine Learning Framework for Time Series Intelligence

  

• anomalib

  17 3,188 9.3 Python

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

  

Project mention: Computer Vision Meetup: Anomaly Detection with Anomalib and FiftyOne | dev.to | 2024-05-10

In this brief walkthrough, I will illustrate how to leverage open-source FiftyOne and Anomalib to build deployment-ready anomaly detection models. First, we will load and visualize the MVTec AD dataset in the FiftyOne App. Next, we will use Albumentations to test out augmentation techniques. We will then train an anomaly detection model with Anomalib and evaluate the model with FiftyOne.

• stumpy

  6 2,998 7.9 Python

  STUMPY is a powerful and scalable Python library for modern time series analysis

  

Project mention: Stumpy: Matrix profile time series analysis | news.ycombinator.com | 2024-03-03

• SaaSHub

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• awesome-TS-anomaly-detection

  72 2,818 0.0

  List of tools & datasets for anomaly detection on time-series data.

Project mention: awesome-TS-anomaly-detection: NEW Data - star count:2694.0 | /r/algoprojects | 2023-11-21

• RubixML

  11 1,977 4.9 PHP

  A high-level machine learning and deep learning library for the PHP language.

  

Project mention: KNN with PHP ML & Rubix ML | dev.to | 2024-05-06

This post is for anybody who has tried to migrate to Rubix ML from PHP ML and more specifically anybody who is experimenting with K-Nearest Neighbors.

• flow-forecast

  13 1,905 9.5 Python

  Deep learning PyTorch library for time series forecasting, classification, and anomaly detection (originally for flood forecasting).

  

• ailia-models

  4 1,840 9.8 Python

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

  

• surpriver

  38 1,672 0.0 Python

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

Project mention: surpriver: Machine learning algo to detect anomaly in equities data. Uses sklearn [IsolationForest](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.IsolationForest.html) model and price/volume based technical signals as features us | /r/algoprojects | 2023-07-08

• loghub

  5 1,537 5.3

  A large collection of system log datasets for AI-driven log analytics [ISSRE'23]

  

• logparser

  2 1,435 7.5 Python

  A machine learning toolkit for log parsing [ICSE'19, DSN'16]

  

Project mention: Log2row: A tool that detects, extracts templates, and structures logs | news.ycombinator.com | 2023-10-06

You use GPT-4 to extract log patterns, does it really need LLM? There are more traditional approach such as https://github.com/logpai/logparser

• tods

  3 1,299 3.1 Python

  TODS: An Automated Time-series Outlier Detection System

  

• graph-fraud-detection-papers

  22 1,283 6.0

  A curated list of graph-based fraud, anomaly, and outlier detection papers & resources

  

• loglizer

  2 1,230 0.0 Jupyter Notebook

  A machine learning toolkit for log-based anomaly detection [ISSRE'16]

  

• pygod

  3 1,217 8.6 Python

  A Python Library for Graph Outlier Detection (Anomaly Detection)

  

Project mention: RAG Using Structured Data: Overview and Important Questions | news.ycombinator.com | 2024-01-10

Ok, using ChatGPT and Bard (the irony lol) I learned a bit more about GNNs:

GNNs are probabilistic and can be trained to learn representations in graph-structured data and handling complex relationships, while classical graph algorithms are specialized for specific graph analysis tasks and operate based on predefined rules/steps.

* Why is PyG it called "Geometric" and not "Topologic" ?

Properties like connectivity, neighborhoods, and even geodesic distances can all be considered topological features of a graph. These features remain unchanged under continuous deformations like stretching or bending, which is the defining characteristic of topological equivalence. In this sense, "PyTorch Topologic" might be a more accurate reflection of the library's focus on analyzing the intrinsic structure and connections within graphs.

However, the term "geometric" still has some merit in the context of PyG. While most GNN operations rely on topological principles, some do incorporate notions of Euclidean geometry, such as:

- Node embeddings: Many GNNs learn low-dimensional vectors for each node, which can be interpreted as points in a vector space, allowing geometric operations like distances and angles to be applied.

- Spectral GNNs: These models leverage the eigenvalues and eigenvectors of the graph Laplacian, which encodes information about the geometric structure and distances between nodes.

- Manifold learning: Certain types of graphs can be seen as low-dimensional representations of high-dimensional manifolds. Applying GNNs in this context involves learning geometric properties on the manifold itself.

Therefore, although topology plays a primary role in understanding and analyzing graphs, geometry can still be relevant in certain contexts and GNN operations.

* Real world applications:

- HuggingFace has a few models [0] around things like computational chemistry [1] or weather forecasting.

- PyGod [2] can be used for Outlier Detection (Anomaly Detection).

- Apparently ULTRA [3] can "infer" (in the knowledge graph sense), that Michael Jackson released some disco music :-p (see the paper).

- RGCN [4] can be used for knowledge graph link prediction (recovery of missing facts, i.e. subject-predicate-object triples) and entity classification (recovery of missing entity attributes).

- GreatX [5] tackles removing inherent noise, "Distribution Shift" and "Adversarial Attacks" (ex: noise purposely introduced to hide a node presence) from networks. Apparently this is a thing and the field is called "Graph Reliability" or "Reliable Deep Graph Learning". The author even has a bunch of "awesome" style lists of links! [6]

- Finally this repo has a nice explanation of how/why to run machine learning algorithms "outside of the DB":

"Pytorch Geometric (PyG) has a whole arsenal of neural network layers and techniques to approach machine learning on graphs (aka graph representation learning, graph machine learning, deep graph learning) and has been used in this repo [7] to learn link patterns, also known as link or edge predictions."

--

0: https://huggingface.co/models?pipeline_tag=graph-ml&sort=tre...

1: https://github.com/Microsoft/Graphormer

2: https://github.com/pygod-team/pygod

3: https://github.com/DeepGraphLearning/ULTRA

4: https://huggingface.co/riship-nv/RGCN

5: https://github.com/EdisonLeeeee/GreatX

6: https://edisonleeeee.github.io/projects.html

7: https://github.com/Orbifold/pyg-link-prediction

• luminol

  3 1,159 1.6 Python

  Anomaly Detection and Correlation library

  

Project mention: How are SREs using AI? | /r/sre | 2023-05-17

We use something (developed in-house) similar to https://github.com/linkedin/luminol for anomaly and correlation.

• telemanom

  14 950 0.0 Jupyter Notebook

  A framework for using LSTMs to detect anomalies in multivariate time series data. Includes spacecraft anomaly data and experiments from the Mars Science Laboratory and SMAP missions.

• ADBench

  1 782 6.9 Python

  Official Implement of "ADBench: Anomaly Detection Benchmark", NeurIPS 2022.

• OpenOOD

  2 755 7.5 Python

  Benchmarking Generalized Out-of-Distribution Detection

Project mention: [Online Leaderboard | Easy Evaluation] OpenOOD v1.5: Enhanced Benchmark for Out-of-Distribution Detection | /r/DeepLearningPapers | 2023-06-28

Open-sourced implementations of 40+ advanced methods (see our repo);

• luminaire

  28 753 0.0 Python

  Luminaire is a python package that provides ML driven solutions for monitoring time series data.

  

• SaaSHub

  www.saashub.com featured

  SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives

  

anomaly-detection related posts

• Computer Vision Meetup: Anomaly Detection with Anomalib and FiftyOne

  1 project | dev.to | 10 May 2024

• Anomalib: Anomaly detection library comprising cutting-edge algorithms

  1 project | news.ycombinator.com | 24 Apr 2024

• RAG Using Structured Data: Overview and Important Questions

  5 projects | news.ycombinator.com | 10 Jan 2024

• Open source anomaly detection for metrics

  1 project | news.ycombinator.com | 18 Dec 2023

• Defect Detection using Computer Vision

  1 project | /r/computervision | 5 Dec 2023

• awesome-TS-anomaly-detection: NEW Data - star count:2694.0

  1 project | /r/algoprojects | 21 Nov 2023

• awesome-TS-anomaly-detection: NEW Data - star count:2694.0

  1 project | /r/algoprojects | 20 Nov 2023
• A note from our sponsor - InfluxDB
  www.influxdata.com | 10 May 2024

  Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality. Learn more →

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