I was just at the international institute for forecasters conference on Colorado as an applied practitioner. There was a lot of hype on how to best use ML/Neur

I was just at the international institute for forecasters conference on Colorado as an applied practitioner. There was a lot of hype on how to best use ML/Neural-nets in time-series forecasting. Well, it was well-founded hype. It was hype by people who know there is potential there, but who also consider a 20% accuracy improvement over naive methods that were developed in the 1980s to be a cause of great success. And even then, it doesn't always work. NN/ML has had true breakthrough success in classification type problems, and fitting situations of nonlinear high-fidelity. For example, self-driving cars. But in terms of economics/demand/weather dynamics, which include much much less data, and often deal with more chaotic macro-patterns (i.e. Your data is a time-series of a few megabytes over years, rather than gigabytes over minutes from cameras), it offers much less.