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Posted by ChernovAndrei 4 days ago

Show HN: Python SDK – forecasting with foundation time-series and tabular models(github.com)
We’ve built a Python SDK for running inference on foundation models designed for time-series and tabular data. They are new SOTA models for time-series and tabular tasks and work out of the box. They do not require model training or feature engineering. The link to the GitHub repository is: https://github.com/S-FM/faim-python-client
13 points | 3 comments
SubiculumCode 4 hours ago|
I do not understand how time series can be forecast without training on data from a relevant domain. Like, would these be able to predict EEG/fMRI timeseries?
armcat 1 hour ago|
The promise is similar to LLMs, if you pretrain on sufficiently large timeseries datasets with sufficiently large variance/characteristics, that you will be able to transfer the model to a completely different use case that exhibits somewhat similar characteristics (in latent space). But it’s always good to check what kind of data the model was trained on, eg Chronos 2.0 training data is described in Appendix A Table 6 here: https://arxiv.org/pdf/2510.15821
BobSonOfBob 1 hour ago|
Would be good if the site had a couple of case studies