159 Open source projects that are alternatives of or similar to Aiopstools

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

This is a times series anomaly detection algorithm, implemented in Python, for catching multiple anomalies. It uses a moving average with an extreme student deviate (ESD) test to detect anomalous points.

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

A curated list of awesome anomaly detection resources

A log analysis toolkit for automated anomaly detection [ISSRE'16]

An End-to-end Outlier Detection System

Python programming assignments for Machine Learning by Prof. Andrew Ng in Coursera

R package for automation of machine learning, forecasting, feature engineering, model evaluation, model interpretation, data generation, and recommenders.

Healthbot configuration examples. Scripts to manage Healthbot. Closed loop automation. Healthbot building blocks description and troubleshooting guide

log anomaly detection toolkit including DeepLog

Detection of Accounting Anomalies using Deep Autoencoder Neural Networks - A lab we prepared for NVIDIA's GPU Technology Conference 2018 that will walk you through the detection of accounting anomalies using deep autoencoder neural networks. The majority of the lab content is based on Jupyter Notebook, Python and PyTorch.

A toolkit for automated log parsing [ICSE'19, TDSC'18, DSN'16]

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

Streaming Anomaly Detection Framework in Python (Outlier Detection for Streaming Data)

Hastic data management server for labeling patterns and anomalies in Grafana

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

Real-World Anomaly Detection in Surveillance Videos

Python codes in Machine Learning, NLP, Deep Learning and Reinforcement Learning with Keras and Theano

Anomaly detection for temporal data using LSTMs

PCA and DBSCAN based anomaly and outlier detection method for time series data.

Log-based Impactful Problem Identification using Machine Learning [FSE'18]

Detect EEG artifacts, outliers, or anomalies using supervised machine learning.

KitNET is a lightweight online anomaly detection algorithm, which uses an ensemble of autoencoders.

RNN based Time-series Anomaly detector model implemented in Pytorch.

Foremast adds application resiliency to Kubernetes by leveraging machine learnt patterns of application health to keep applications healthy and stable

Source code for Skip-GANomaly paper

A Python toolkit for rule-based/unsupervised anomaly detection in time series

Latent space autoregression for novelty detection.

[5 FPS - 150 FPS] Learning Deep Features for One-Class Classification (AnomalyDetection). Corresponds RaspberryPi3. Convert to Tensorflow, ONNX, Caffe, PyTorch. Implementation by Python + OpenVINO/Tensorflow Lite.

Isolation Forest on Spark

Implementation of Visual Feature Attribution using Wasserstein GANs (VAGANs, https://arxiv.org/abs/1711.08998) in PyTorch

A Python 3 library making time series data mining tasks, utilizing matrix profile algorithms, accessible to everyone.

Probabilistic programming framework that facilitates objective model selection for time-varying parameter models.

Go implementation of MIDAS: Microcluster-Based Detector of Anomalies in Edge Streams

Kibana Alert & Report App for Elasticsearch

Abnormal Event Detection in Videos using SpatioTemporal AutoEncoder

Time series based anomaly detector

Repository for the Deep One-Class Classification ICML 2018 paper

A machine learning plugin in Open Distro for Elasticsearch for real time anomaly detection on streaming data.

A Spark/Scala implementation of the isolation forest unsupervised outlier detection algorithm.

Repository for the Explainable Deep One-Class Classification paper

Anomaly detection analysis and labeling tool, specifically for multiple time series (one time series per category)

An Unsupervised Graph-based Toolbox for Fraud Detection

BERT, AWS RDS, AWS Forecast, EMR Spark Cluster, Hive, Serverless, Google Assistant + Raspberry Pi, Infrared, Google Cloud Platform Natural Language, Anomaly detection, Tensorflow, Mathematics

AIOps学习资料汇总，欢迎一起补全这个仓库，欢迎star

Unsupervised anomaly detection with generative model, keras implementation

Anomaly Detection and Correlation library

A robust, and flexible open source User & Entity Behavior Analytics (UEBA) framework used for Security Analytics. Developed with luv by Data Scientists & Security Analysts from the Cyber Security Industry. [PRE-ALPHA]

An open-source framework for real-time anomaly detection using Python, ElasticSearch and Kibana

Open-source framework to detect outliers in Elasticsearch events

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 BERT.

CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances (NeurIPS 2020)

A collection of anomaly detection methods (iid/point-based, graph and time series) including active learning for anomaly detection/discovery, bayesian rule-mining, description for diversity/explanation/interpretability. Analysis of incorporating label feedback with ensemble and tree-based detectors. Includes adversarial attacks with Graph Convolutional Network.

A PyTorch implementation of Deep SAD, a deep Semi-supervised Anomaly Detection method.

Deep Learning sample programs using PyTorch in C++

Unofficial Tensorflow Implementation of AnoGAN (Anomaly GAN)

Pytorch Implementation of DeepLog.

Log Anomaly Detection - Machine learning to detect abnormal events logs

Anomaly detection for streaming time series, featuring automated model selection.

Generative Probabilistic Novelty Detection with Adversarial Autoencoders