I'm curious where universal forecasting models are most useful. It is technically fascinating but forecasting specifically seems like a domain where you'd want interpretable modeling - you use it for big-value problems and it significantly affects your action/policy. So, the tradeoff between performance and model simplicity should lean towards the latter?
Same for my shop - we manage a large pool of cost driven by partially forcastable factors; we've repeatedly rejected methods purely on explainability grounds. Our accountability requirements do not allow us to point the finger at an LLM if we get it wrong.
