Research paper / RESOURCE PROFILE

Moirai-MoE: Sparse Mixture of Experts for TS

Adds sparse mixture-of-experts routing to time-series foundation models.

arxiv.orgPublished Oct 14, 2024
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Overview

Adds sparse mixture-of-experts routing to time-series foundation models.

Topics & capabilities

foundation-modelpapermixture-of-experts

Publication information

Publication title
Moirai-MoE: Empowering Time Series Foundation Models with Sparse Mixture of Experts
Authors
Xu Liu, Juncheng Liu, Gerald Woo, Taha Aksu, Yuxuan Liang, Roger Zimmermann, Chenghao Liu, Silvio Savarese, Caiming Xiong, Doyen Sahoo
Published
Oct 14, 2024
Last publication revision
Oct 14, 2024
Subject areas
cs.LG, stat.ML
Publication source
arxiv.org · abs/2410.10469v1 ↗

Source: arXiv API · observed Oct 11, 2026.

Source links & provenance

https://arxiv.org/abs/2410.10469 ↗

Discovery references

References explain how this resource entered the collection. They do not represent an endorsement.

Explore related resources

Screenshot unavailableResource information and source links remain available. DeepIntoStreams/TSFM_Finance — reviewed source website screenshot
RepositoryChecked capture

DeepIntoStreams/TSFM_Finance

github.com

the official implementation of the paper “Re(visiting) Time Series Foundation Models in Finance”.

GitHub stars
31
Language
Jupyter Notebook
Code license
Apache-2.0
foundation-modeltime-seriesresearch-lab
96 Authority 96 / 100 Cited by 2 resources in this directory 2 discovery sources Directory record updated: 2026-10-11 How scoring works →

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