Add ensemble method
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- Dominant language
- Python
- Stars
- 109
- Forks
- 26
- Avg merge
- 3d 4h
- Merged PRs (30d)
- 6
Description
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
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by reviewing the existing forecasting endpoints for statsforecast, mlforecast, and neuralforecaster to understand how individual models produce outputs. Define the scope for simple averaging and greedy local/global ensembles, including how prediction intervals and models from different repositories should be handled; completion requires those design questions to be resolved.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- data, machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100