536 Open source projects that are alternatives of or similar to Anomalize

Resources for working with time series and sequence data

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

Repository of the paper "A Systematic Evaluation of Deep Anomaly Detection Methods for Time Series".

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.

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

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

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

High-performance time series algorithms for Ruby

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

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

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 repository for code accompanying the manuscript 'An Evaluation of Anomaly Detection and Diagnosis in Multivariate Time Series' (published at TNNLS)

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

Library for multi-dimensional, multi-sensor, uni/multivariate time series data analysis, unsupervised feature selection, unsupervised deep anomaly detection, and prototype of explainable AI for anomaly detector

PyTorch implementation of MTAD-GAT (Multivariate Time-Series Anomaly Detection via Graph Attention Networks) by Zhao et. al (2020, https://arxiv.org/abs/2009.02040).

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

Anomaly detection for temporal data using LSTMs

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.

An End-to-end Outlier Detection System

fast implementation of singular spectrum transformation (change point detection algorithm)

Merlion: A Machine Learning Framework for Time Series Intelligence

Graph-based Knowledge Tracing: Modeling Student Proficiency Using Graph Neural Network

This repository is the PyTorch implementation of GAN Ensemble for Anomaly Detection.

Remaining Useful Life (RUL) estimation of Lithium-ion batteries using deep LSTMs

Forecasting 🇫🇷 elections with Bayesian statistics 🥳

Process live and historical data from luftdaten.info, IRCELINE and OpenAQ. Filter by station-id, sensor-id and sensor-type, apply reverse geocoding, store into timeseries and RDBMS databases, publish to MQTT, output as JSON or visualize in Grafana.

Unsupervised Anomaly Detection with Generative Adversarial Networks on MIAS dataset

ADRepository: Real-world anomaly detection datasets

Official codes for CVPR2021 paper "MIST: Multiple Instance Self-Training Framework for Video Anomaly Detection"

A thesis submitted for the degree of Master of Science in Computer Networks and Security

Supplementary material for KDD 2018 workshop "DCSO: Dynamic Combination of Detector Scores for Outlier Ensembles"

Thio - a playground for real-time anomaly detection

Particle filtering and sequential parameter inference in Python

(Python, R, C++) Explainable outlier/anomaly detection through decision tree conditioning

THIS REPO HAS MOVED TO https://github.com/apache/incubator-iotdb. TsFile is a columnar file format designed for time-series data, which supports efficient compression and query. It is easy to integrate TsFile with your IOT big data processing frameworks.

Nonlinear Nonparametric Statistics

📈 high level wrapper for parallel univariate time series forecasting 📉

Useful Toolbox for Anomaly Detection

MODIS Assimilation and Processing Engine

Calculates the Hurst exponent of a time series based on Rescaled range (R/S) analysis.

Calculates various features from time series data. Python implementation of the R package tsfeatures.

Machine Learning Projects

📈 PyTorch dual-attention LSTM-autoencoder for multivariate Time Series 📈

Benchmark for DeepLearning anomaly detection

A framework for historical volatility estimation and analysis.

Analyzes chronological patterns present in time-series data and provides human-readable descriptions

Machine learning models for time series analysis

This project proposes an end-to-end framework for semi-supervised Anomaly Detection and Segmentation in images based on Deep Learning.

Visualize large time-series data in plotly

Faster and more precisely than Grad-CAM

A flexible data historian based on InfluxDB, Grafana, MQTT and more. Free, open, simple.

A Python interface between Earth Engine and xarray for processing time series data

A Julia implementation of basic tools for time series analysis compatible with incomplete data.

R interface to Brazilian Central Bank web services

Using Kalman Filter to Predict Corona Virus Spread

An interactive 3D animation using WebGL to depict a 2D predator prey ecology on a grid real-time mapped onto the surface of a 3D torus. Sound file is parsed then visualized both in time and frequency domains as well as rendered using Web Audio API - this is an exercise where I taught myself how to display data for an ongoing project on sound syn…

使用极端值理论(Extreme Value Theory)实现阈值动态自动化设置

[Implementation example] Attend and Diagnose: Clinical Time Series Analysis Using Attention Models

Barrage is an opinionated supervised deep learning tool built on top of TensorFlow 2.x designed to standardize and orchestrate the training and scoring of complicated models.

Exponential Smoothing & Moving Average Models in Java