Data Science

Detecting spacecraft anomalies using LSTMs and nonparametric dynamic thresholding

~ Adrian Colyer ~ 3 Comments
Detecting spacecraft anomalies using LSTMs and nonparametric dynamic thresholding Hundman et al., KDD'18 How do you effectively monitor a spacecraft? That was the question facing NASA’s Jet Propulsion Laboratory as they looked forward towards exponentially increasing telemetry data rates for Earth Science satellites (e.g., around 85 terabytes/day for a Synthetic Aperture Radar satellite). Spacecraft are ... Continue Reading

I know you’ll be back: interpretable new user clustering and churn prediction on a mobile social application

~ Adrian Colyer ~ 2 Comments
I know you’ll be back: interpretable new user clustering and churn prediction on a mobile social application Yang et al., KDD'18 Churn rates (how fast users abandon your app / service) are really important in modelling a business. If the churn rate is too high, it’s hard to maintain growth. Since acquiring new customers is ... Continue Reading

Unsupervised anomaly detection via variational auto-encoder for seasonal KPIs in web applications

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Unsupervised anomaly detection via variational auto-encoder for seasonal KPIs in web applications Xu et al., WWW'18 (If you don’t have ACM Digital Library access, the paper can be accessed either by following the link above directly from The Morning Paper blog site, or from the WWW 2018 proceedings page). Today’s paper examines the problem of ... Continue Reading

Can you trust the trend? Discovering Simpson’s paradoxes in social data

~ Adrian Colyer ~ 6 Comments
Can you trust the trend? Discovering Simpson’s paradoxes in social data Alipourfard et al., WSDM’18 In ‘Same stats, different graphs,’ we saw some compelling examples of how summary statistics can hide important underlying patterns in data. Today’s paper choice shows how you can detect instances of Simpson’s paradox, thus revealing the presence of interesting subgroups, ... Continue Reading