Papers i’m reading #2

Continuation from this post. An unsupervised spatiotemporal graphical modeling approach to anomaly detection in distributed CPS (Cyber Physical Systems). Link My Summary: Really interesting paper - PGM’s, HMM’s and all that good stuff. Quite complicated though and no clear route to implementation. Also I would wonder how well it scales beyond 10’s of time series. … Continue reading Papers i’m reading #2 →

KubeFlow Custom Jupyter Image (+ github for notebook source control)

I've been playing around a bit with KubeFlow a bit lately and found that a lot of the tutorials and examples of Jupyter notebooks on KubeFlow do a lot of the pip install and other sort of setup and config stuff in the notebook itself which feels icky. But, in reality, if you were working … Continue reading KubeFlow Custom Jupyter Image (+ github for notebook source control) →

Multi-Variate, Multi-Step, LSTM for Anomaly Detection

This post will walk through a synthetic example illustrating one way to use a multi-variate, multi-step LSTM for anomaly detection. Imagine you have a matrix of k time series data coming at you at regular intervals and you look at the last n observations for each metric. A matrix of 5 metrics from period t to t-n One approach … Continue reading Multi-Variate, Multi-Step, LSTM for Anomaly Detection →