Yeah, that’s part of the game though. You can’t get perfection from modelling a complex system with the (relatively) few variables that you can actually measure. Assumptions are always evolving, and are always going to be broken at some point or another.
That’s the opening line, right? Uncertainty is a fact of life. With time series forecasts, the best you can ever hope to do is give probability bounds, and even then you can only really do so by either:
- limiting by the rules of the game (e.g. the laws of physics, or the rules of a stock exchange)
- using past data
The former is only useful if you’re the most risk averse person on the planet, and the latter is only useful if you are willing to assume the past is relevant.
Good response. People seem to think that what I call “single pass” inference is the only thing that matters - a monolithic single process system
When in fact the world and intelligent agents inside it are ensembles of ensembles of systems with various and changing confidence that flow and adjust as the world does
That’s the opening line, right? Uncertainty is a fact of life. With time series forecasts, the best you can ever hope to do is give probability bounds, and even then you can only really do so by either:
- limiting by the rules of the game (e.g. the laws of physics, or the rules of a stock exchange)
- using past data
The former is only useful if you’re the most risk averse person on the planet, and the latter is only useful if you are willing to assume the past is relevant.