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yzhao062/pyod: A Python Toolkit for Scalable Outlier Detection (Anomaly Detection)
PyOD is a comprehensive and scalable Python toolkit for detecting outlying objects in multivariate data. This exciting yet challenging field is commonly referred as Outlier Detection or Anomaly Detection. Since 2017, PyOD has been successfully used in various academic researches and commercial products. PyOD is featured for:

Unified APIs, detailed documentation, and interactive examples across various algorithms.
Advanced models, including Neural Networks/Deep Learning and Outlier Ensem...
anomaly  anomaly-detection  machinelearning  python  deeplearning  neuralnetwork  github 
5 days ago by newtonapple
MentatInnovations/datastream.io: An open-source framework for real-time anomaly detection using Python, ElasticSearch and Kibana
An open-source framework for real-time anomaly detection using Python, ElasticSearch and Kibana - MentatInnovations/datastream.io
anomaly-detection  anomaly  python  elasticsearch  kibana  stream  timeseries  github 
5 days ago by newtonapple
linkedin/luminol: Anomaly Detection and Correlation library
Luminol is a light weight python library for time series data analysis. The two major functionalities it supports are anomaly detection and correlation. It can be used to investigate possible causes of anomaly. You collect time series data and Luminol can:

Given a time series, detect if the data contains any anomaly and gives you back a time window where the anomaly happened in, a time stamp where the anomaly reaches its severity, and a score indicating how severe is the anomaly compare to ...
anomaly  anomaly-detection  linkedin  python  timeseries  library 
5 days ago by newtonapple
Detecting Anomalies in Time Series Data: Deciphering the Noise and Zoning in on the Signals - insideBIGDATA
While each of the above techniques obviously has advantages as well as disadvantages, it’s only unsupervised anomaly detection that is feasible in the case of raw, unlabelled time series data – which is what you get from just about any online asset in a modern-day digitised company. Anomaly detection in time series data has a variety of applications across industries – from identifying abnormalities in ECG data to finding glitches in aircraft sensor data.
deeplearning  machinelearning  ml  anomalydetection  anomaly  unsupervised 
12 days ago by dlkinney
Time Series Anomaly Detection Algorithms – Stats and Bots
First, you can use supervised learning to teach trees to classify anomaly and non-anomaly data points. In order to do that you’d need to have labeled anomaly data points.
ml  algorithms  alerting  anomaly  stats  time  series 
22 days ago by dano
Quantum Physicists Found a New, Safer Way to Navigate | WIRED
Quantum magnetometer showing strength and direction, mapped to know earth mag anomalies, as well as a quantum laser rate gyro that requires no drift resets.
quantum  sensor  navigation  magnetometer  earth  anomaly  field  mapping  laser  rate  gyro  diffraction  pattern  hardware  electronics  devices  research  technology 
11 weeks ago by asteroza
Detecting spacecraft anomalies using LSTMs and nonparametric dynamic thresholding
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
LSTM  Anomaly  Detection 
october 2018 by FredericJacobs
Anomaly Detection & Threat Hunting with Anomalize - SANS Internet Storm Center
Matt Dancho (@mdancho84). He created anomalize, "a tidy anomaly detection algorithm that’s time-based (built on top of tibbletime) and scalable from one to many time series," when a client asked Business Science to build an open source anomaly detection algorithm that suited their needs
dfir  cybersecurity  anomaly  sans  threathunting  r  analytics 
july 2018 by bwiese

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