[a-dub]

something like that would be interesting. i just get the sense that doing it metadata blind would result in something that learns a somewhat brittle short-time covariance-like structure that doesn't use all available information to achieve best possible generalization and that in the end the benchmark numbers start to reflect some luck with that for predicting non-stationary behavior combined with a lot coming from a pretty good synthesizer that matches previous patterns.

that being the case, it seems the next jump in performance would come from incorporating both metadata and metadata enriched causality and maybe that next jump in performance would be the most interesting jump from a practical system that is useful perspective. (it's more valuable for a system to predict outlier events than it is for it to do an excellent job at synthesizing ordinary behavior)

[thomasikzelf]

I did some energy price forecasting and my neural network learned the morning evening patterns just from historical data, it worked great. Then I tried adding more information to this like wind and solar (and many more). None of these external factors made any difference while being very important for energy prices. It seems like the market already incorporated all the factors in the price (as they should).

The takeaway might be that historic data of market data might often be enough to make a reasonable prediction. Only external data that nobody else has (used) can make your prediction better then the market.

Making a prediction with same accuracy as the market: easy Making a prediction with more accuracy then the market: very hard

[a-dub]

> The takeaway might be that historic data of market data might often be enough to make a reasonable prediction. Only external data that nobody else has (used) can make your prediction better then the market.

that's basically the definition of an efficient market. demand forecasting and insuring against price shifts is the actually useful thing that the commodities futures markets do.

also a fundamental difference from other time series prediction problems. there are all sorts of weird dynamics that go on before one biosignal effects another, or one metric predicts a failure, where a reasonably efficient market price reacts quickly to well known exogenous factors.