Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

[Core] Make the library compatible with AnyDataframe (spark, ray, dask) #28

Open
AzulGarza opened this issue Jun 8, 2023 · 2 comments

Comments

@AzulGarza
Copy link
Member

Description

Currently, tsfeatures utilizes a map-reduce approach and multiprocessing to compute several features for different time series. However, the implementation is currently only supported for pandas. By incorporating fugue, we can ensure tsfeatures compatibility with spark, ray, and dask.

For reference on how the implementation should look, please see https://github.com/Nixtla/statsforecast/blob/main/statsforecast/core.py#L1784.

Use case

No response

@webert6
Copy link

webert6 commented Feb 19, 2024

What's the status on this one?

@mergenthaler
Copy link
Member

Hi @webert6, this is currently in the backlog. Are you interested in working on this?

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Projects
None yet
Development

No branches or pull requests

3 participants