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ChernovAndrei

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Submissions

Show HN: Store and reuse your Claude Code plans

github.com
1 points·by ChernovAndrei·vor 4 Monaten·0 comments

Show HN: Python SDK – forecasting with foundation time-series and tabular models

github.com
43 points·by ChernovAndrei·vor 7 Monaten·18 comments

Show HN: UI front end to forecast with foundation time-series models

faim.it.com
1 points·by ChernovAndrei·vor 7 Monaten·0 comments

Show HN: MCP Server for Time-Series Forecasting

github.com
1 points·by ChernovAndrei·vor 8 Monaten·1 comments

Show HN: JavaScript SDK for zero-shot time-series forecasting (Chronos2, TiRex)

github.com
2 points·by ChernovAndrei·vor 8 Monaten·0 comments

Show HN: Serverless platform for inference of time-series foundation models

faim.it.com
1 points·by ChernovAndrei·vor 8 Monaten·0 comments

comments

ChernovAndrei
·vor 7 Monaten·discuss
There is no single answer, because there are multiple architectures for foundation time-series models, such as T5, decoder-only models, and state-space models (SSMs).

For Chronos-2 (the current state of the art in time-series modeling), the setup is almost identical to that of LLMs because it is based on the T5 architecture. The main difference is that, in time-series models, tokens correspond to subintervals in the real-valued (ℝ) space. You can check the details here: https://arxiv.org/pdf/2510.15821
ChernovAndrei
·vor 7 Monaten·discuss
Limix outperforms tabpfn v2: https://arxiv.org/pdf/2509.03505