ML Algorithm for anomaly detection in paired time-series I have many (around 40) separate time-series from different sensors, each measuring magnetic field intensity. I am looking to get an ML algorithm to identify a particular anomaly. This anomaly happens infrequently (a handful of times in 25 years of 60-second data, depending on the sensor) and is only apparent when you look at two time-series side-by-side:



The yellow circle is the anomaly, and can be described roughly like this: when the time series are particularly disturbed, the red time-series will briefly anti-correlate with the green time-series over a period of a few hours (data are sampled every 60s). This can easily be done by hand for a pair of time-series.

The Plan

I'd like to train a machine learning algorithm by manually identifying these anomalies for a sensor pair, and then set the trained algorithm on the rest of the pairs of time-series

The Problem

I have no idea what the best algorithm would be for this problem, as it involves two time-series. I messed around with gathering information in 6 hour blocks and treating it like a non-time series problem. This involved treating each 6-hour block separately (i.e., each with std, average, max, min of each time-series as well as a correlation coeff and a 1 or 0 output determined manually), but I feel like there is a neural network that will be able to better figure out the important variables.

The Question

What would be the best approach to this problem in order to train a ML algo? Is my approach above suitable?
Before think which algorithm, think what features. You may add/ignore features that imply your problem fluently. 

This will be around quantifying anomalies (new variable?). Try to add a residual variable? Or simply feature different between red and green variables?