Pinboard (jm)
https://pinboard.in/u:jm/public/
recent bookmarks from jmIntroducing practical and robust anomaly detection in a time series2015-01-07T22:42:32+00:00
https://blog.twitter.com/2015/introducing-practical-and-robust-anomaly-detection-in-a-time-series
jmEarly detection of anomalies plays a key role in ensuring high-fidelity data is available to our own product teams and those of our data partners. This package helps us monitor spikes in user engagement on the platform surrounding holidays, major sporting events or during breaking news. Beyond surges in social engagement, exogenic factors – such as bots or spammers – may cause an anomaly in number of favorites or followers. The package can be used to find such bots or spam, as well as detect anomalies in system metrics after a new software release. We’re open-sourcing AnomalyDetection because we’d like the public community to evolve the package and learn from it as we have.
]]>statistics twitter r anomaly-detection outliers metrics time-series spikes holt-wintershttps://pinboard.in/https://pinboard.in/u:jm/b:569f792516a5/'Histogram-based Outlier Score (HBOS): A fast Unsupervised Anomaly Detection Algorithm' [PDF]2014-11-18T14:18:47+00:00
http://www.dfki.de/KI2012/PosterDemoTrack/ki2012pd13.pdf
jmhistograms anomaly-detection anomalies machine-learning algorithms via:paperswelove outliers unsupervised-learning hboshttps://pinboard.in/https://pinboard.in/u:jm/b:ed161c3b8a77/