6-27B backbone (a large language model).
Mixture of experts unifies time-series forecasting and reasoning in one model
OpenTSLM TeeMoE independently trains three low-rank experts for forecast aggregation, native forecasting, and temporal analysis, then composes them with a learned controller to achieve top-three results across three distinct benchmarks.
Big Tech
Tony Chen · Timo Stoffregen · Maxwell Xu · Thomas Kaar · Martin Maritsch · Geremia Pompei · +5 more
Columbia University · Stanford University · Aionic Labs · Google Agentic Systems Lab · ETH Zürich
Research Digest··3 min read
6-27B backbone.
Why this paper
From Google Agentic Systems Lab and 6 others
In one line
A single time-series language model matches top specialized systems in forecasting, contextual prediction, and reasoning by mixing independently trained experts.
What we could check
- ·No code link found
- ·No weights link found
- ·No dataset link found
- ·No compute details found
- ·No stated limitations found
- ·No benchmark numbers found
Observed from the paper text and links we have. Absence here means we did not find it, not that it does not exist.
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