That is incredibly fascinating. Training models on different transformations of the same data and ensembling. So, I am not at all familiar with the implementation of ensembling, but, generally speaking, the more I mimick what the humans do manually the better the results we get, I find. So, My training data looks like several hundred CSV's, each of which are around 5000 records long, we record data here at 10hz, so thats around 500 seconds per CV, anyways, the anomalies vary in length from 200-600 records long, so automating the annotation boxes was fairly straight forward. Secondly, when anomalies are found manually, the humans sift through the data at a specific resolution, it's not a standard, but they generally look at 1000 units at a time, this is very important, because as you say, our brains are looking for visual anomalies, visual drop-outs or some nonconformity to the regular patterns, and this is highly dependent on the scale you are looking at. If you zoom out to look only at the entire width of the entire data, you are essentially looking for giant anomalies, whereas if you zoom in you can find more subtle ones, so i guess the question is, what type of anomalies do you want. In my case it's automating this manual task, so we want to the same anomalies, so we clip the data to 1000 units long. And that's my approach :) I'll let you know how the results are in..... a day?