[srean]

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]

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 that’s cool modeling and a big compute spend rather than good results.

[anshumankmr]

Without getting into specifics (just joined a new firm), we’re working with a bunch of billing data.

Management is leaning toward a deep learning forecasting approach — train a neural net to predict expected cost and then use multiple deviation scorers (including Wasserstein distance) to flag anomalies.

A simpler v1 is already live, and this newer approach isn’t my call. I’m still fairly new to anomaly detection, so for now I’m mostly trying to learn and ship within the existing direction rather than fight it.