Description
Current end points for forecasting (statsforecast, mlforecast, neuralforecaster) only allow usage of individual models. I am proposing the option for ensembling multiple models into one model output (as offered for example by AutoGluon).
I think, what would be an good starting point would be a simple average and the implementation of the greedy ensemble (local and global).
Open questions for me at this point are:
- How to deal with prediction intervals (e.g.: conformal predictions) when combining different models?
- Would there be a way to ensemble models from different repos like statsforecast and mlforecast?
Use case
No response
Description
Current end points for forecasting (statsforecast, mlforecast, neuralforecaster) only allow usage of individual models. I am proposing the option for ensembling multiple models into one model output (as offered for example by AutoGluon).
I think, what would be an good starting point would be a simple average and the implementation of the greedy ensemble (local and global).
Open questions for me at this point are:
Use case
No response