Benedikt Heidrich
I completed my PhD in deep learning based time series forecasting in 2023 with the Karlsruhe Institute of Technology. In sktime, I am focusing on forecasting methods (mainly deep learning based ones) and implementing pipelines.
Session
Foundation models are here for forecasting! This will conclusively solve all forecasting problems with a one-model-fits-all approach! Or … maybe not?
Fact is, an increasingly growing number of foundation models for time series and forecasting hitting the market.
To innocent end users, this situation raises various challenges and questions. How do I integrate the models as candidates into existing forecasting workflows? Are the models performant? How do they compare to more classical choices? Which one to pick? How to know whether to “upgrade”?
At sktime, we have tried so you don’t have to! Although you will probably be forced to anyway, but even then, it’s worth sharing experiences.
Our key challenges and findings are presented in this talk – for instance, the unexpected fragmentation of the ecosystem, difficulties in evaluating the models fairly, and more.
(sktime is an openly governed community with neutral point of view. You may be surprised to hear that this talk will not try to sell you a foundation model)