> Because many of these have the same underlying causal structures - humans doing things, weather correlations, holidays.

Or, you know, maybe they aren't. Thermometers and photon counts are related to weather sometimes, but not holidays. Holidays are related to traffic sensors and to markets, but not Geiger counters.

> Well studied behavioral stuff like "the stock market takes the stairs up and the elevator down" which is not really captured by "traditional" modelling tools.

Prices are the opposite, up like a shot during shocks, falling slowly like a feather. So that particular pattern seems like a great example of over-fitting danger and why you wouldn't expect mixing series of different types to be work very well.

Electricity demand is influenced very strongly by holidays, strongly by weather and from weak to strong by geopolitics (depending on location).

The model will have a library of patterns, and will be able to pattern match subtle ones to deduce "this time series has the kind of micro-patterns which appear in strongly weather influenced time-series", and use this to activate the weather pattern cluster.

To use your example, when served thermometer data, the model notices that the holiday pattern cluster doesn't activate/match at all, and will ignore it.

And then it makes sense to train it on the widest possible time series, so it can build a vast library of patterns and find correlations of activation between them.