arXiv：cs.LG· Tony Chen, Timo Stoffregen, Maxwell Xu, Thomas Kaar, Martin Maritsch, Geremia Pompei, Nicolas Zumarraga, Robert Jakob, Paul Schmiedmayer, Patrick Langer, Juncheng Liu·· 4 小时前AI 评分45
OpenTSLM TeeMoE：统一时序语言模型，兼顾预测、上下文预测与推理
OpenTSLM TeeMoE: A Unified Time-Series Language Model for Forecasting, Contextual Prediction, and Reasoning
AI 导读
OpenTSLM TeeMoE 是一个通用时序语言模型，可基于观测时序直接预测、结合文本上下文与时间模式进行推理，并整合外部数值预测专家的结果。模型在共享骨干上独立训练预测聚合、原生预测和时间分析三个低秩专家，由学习到的 LoRA 混合专家控制器按请求加权其冻结参数更新。
正文
Authors:Tony Chen, Timo Stoffregen, Maxwell Xu, Thomas Kaar, Martin Maritsch, Geremia Pompei, Nicolas Zumarraga, Robert Jakob, Paul Schmiedmayer, Patrick Langer, Juncheng Liu
Abstract:Real-world time-series applications increasingly require models that can handle time series forecasting, context-conditioned prediction, and language-based temporal reasoning. Yet current time-series foundation models remain fragmented across these capabilities: numerical specialists often provide the strongest forecasts, while language-based models offer broader contextual understanding and analysis. A central challenge is to unify these heterogeneous capabilities without reducing their individual performance. We introduce OpenTSLM TeeMoE, a generalist time-series language model that can forecast directly from observed time series, reason over textual context and temporal patterns, and synthesize and refine predictions from external numerical forecasting specialists. We independently train three low-rank experts for forecast aggregation, native forecasting, and temporal analysis over a shared backbone. A learned LoRA mixture-of-experts controller then weights their frozen parameter updates for each request. Our proposed model achieves strong performance on widely used benchmarks for time series forecasting, context-conditioned prediction, and language-based temporal reasoning, ranking among the top three on GIFT-Eval by mean MASE rank, Context is Key by RCRPS, and TimeSeriesExam by accuracy.
| Comments: | 39 pages, 2 figures. Code: this https URL ; model: this https URL |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2609.40265 [cs.LG] |
| (or arXiv:2609.40265v2 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2609.40265 arXiv-issued DOI via DataCite |
Submission history
From: Tony Chen [view email]
[v1]
Wed, 30 Sep 2026 17:43:22 UTC (181 KB)
[v2]
Wed, 7 Oct 2026 17:19:33 UTC (182 KB)
来源：arXiv：cs.LG · arxiv.org