anomaly-detection

Open-source projects categorized as anomaly-detection
Edit details
Language: + Python + Jupyter Notebook + PHP
Topics: Machine Learning outlier-detection Deep Learning time-series Python
SaaSHub - Software Alternatives and Reviews
SaaSHub helps you find the best software and product alternatives
www.saashub.com
featured

Top 23 anomaly-detection Open-Source Projects

  • pycaret

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

  • pyod

    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

    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.

    InfluxDB logo

  • anomaly-detection-resources

    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

    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

    Merlion: A Machine Learning Framework for Time Series Intelligence

  • anomalib

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

    • Project mention: May 8, 2024 AI, Machine Learning and Computer Vision Meetup | dev.to | 2024-05-01

    This talk highlights the role of Anomalib, an open-source deep learning framework, in advancing anomaly detection within AI systems, particularly showcased at the upcoming CVPR Visual Anomaly and Novelty Detection (VAND) workshop. Anomalib integrates advanced algorithms and tools to facilitate both academic research and practical applications in sectors like manufacturing, healthcare, and security. It features capabilities such as experiment tracking, model optimization, and scalable deployment solutions. Additionally, the discussion will include Anomalib’s participation in the VAND challenge, focusing on robust real-world applications and few-shot learning for anomaly detection.

  • stumpy

    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

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

    SaaSHub logo

  • awesome-TS-anomaly-detection

    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

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

    • Project mention: Machine learning and deep learning library for the PHP language | news.ycombinator.com | 2023-11-04

  • flow-forecast

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

  • ailia-models

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

  • surpriver

    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

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

  • logparser

    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

    TODS: An Automated Time-series Outlier Detection System

  • graph-fraud-detection-papers

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

  • loglizer

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

  • pygod

    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

    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

    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

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

  • luminaire

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

  • OpenOOD

    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);

  • SaaSHub

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

    SaaSHub logo
NOTE: The open source projects on this list are ordered by number of github stars. The number of mentions indicates repo mentiontions in the last 12 Months or since we started tracking (Dec 2020).

anomaly-detection related posts

Index

What are some of the best open-source anomaly-detection projects? This list will help you:

Project Stars
1 pycaret 8,428
2 pyod 7,962
3 anomaly-detection-resources 7,871
4 darts 7,294
5 Merlion 3,266
6 anomalib 3,154
7 stumpy 2,994
8 awesome-TS-anomaly-detection 2,811
9 RubixML 1,975
10 flow-forecast 1,900
11 ailia-models 1,825
12 surpriver 1,672
13 loghub 1,524
14 logparser 1,433
15 tods 1,297
16 graph-fraud-detection-papers 1,278
17 loglizer 1,228
18 pygod 1,208
19 luminol 1,159
20 telemanom 950
21 ADBench 777
22 luminaire 753
23 OpenOOD 751

Sponsored
SaaSHub - Software Alternatives and Reviews
SaaSHub helps you find the best software and product alternatives
www.saashub.com