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Massive Scale Anomaly Detection Framework
Guy Gerson introduces an anomaly detection framework PayPal uses, focusing on flexibility to support different types of statistical and ML models, and inspired by scikit-learn and Spark MLlib.
anomaly_detection  anomaly  outlier  ml  mlops 
3 days ago by izgurskii
Gravity ‘anomaly’ at Moon’s south pole could be buried metallic asteroid • Extreme Tech
Ryan Whitwam:
<p>The leading explanation for the gravitational anomaly, <a href="https://www.baylor.edu/mediacommunications/news.php?action=story&story=210457">according to the researchers</a>, is that the object responsible for the crater is still mostly intact beneath the surface. So, some 4 billion years ago, a mostly metallic asteroid hit the moon and remains embedded in the mantle to this day. Another potential explanation is that the region is naturally rich in oxides that formed as the moon cooled in the distant past. However, the overlap of the crater and increased gravity seems a bit too convenient.

If there is a large metallic object buried under the South Pole-Aitken basin, it could tell us something about the moon’s interior. After four billion years, the iron-nickel remains of the asteroid would have been dispersed throughout the mantle if the moon was geologically active for any significant period of time.</p>


Ooooh is it a radio-opaque obelisk with proportions of 1:4:9? Looking forward to the expedition visiting it.
moon  anomaly  asteroid 
6 days ago by charlesarthur
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 
january 2019 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 
january 2019 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 
january 2019 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  timeseries 
january 2019 by dlkinney

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