>We have successfully replaced thousands of complicated deep net time series based anomaly detectors at a FANG with statistical (nonparametric, semiparametric) process control ones.

They use 3 to 4 orders lower number of trained parameters and have just enough complexity that a team of 3 or four can handle several thousands of such streams.

Could you explain how ? Cause I am working on this essentially right now and it seems management is wanting to go the way of Deep NNs for our customers.

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srean 3 hours ago | parent [-]

Without knowing details it's very hard to give specific recommendations. However if you follow that thread you will see folks have commented on what has worked for them.

In general I would recommend get Hyndman's (free) book on forecasting. That will definitely get you upto speed.

https://news.ycombinator.com/item?id=46058611

Wishing you the best.

If it's the case that you will ship the code over client's fence and be done with it, that is, no commitments regarding maintenance, then I will say do what the management wants. If you will continue to remain responsible for the ongoing performance of the tool then you will be better if choosing a model you understand.

	▲	clickety_clack 14 minutes ago \| parent [-]
		MBAs do love their neural nets. As a data scientist you have to figure out what game you’re playing: is it the accuracy game or the marketing game? Back when I was a data scientist, I got far better results from “traditional” models than NN, and I was able to run off dozens of models some weeks to get a lot of exposure across the org. Combined with defensible accuracy, this was a winning combination for me. Sometimes you just have to give people what they want, and sometimes they want cool modeling and a big compute spend rather than good results.