OpenTSLM TeeMoE统一时间序列语言模型
OpenTSLM TeeMoE: A Unified Time-Series Language Model for Forecasting, Contextual Prediction, and Reasoning
OpenAI发布TeeMoE模型，能同时处理时间序列预测、上下文预测和语言推理，在三个基准测试中排名前三。
OpenTSLM TeeMoE是一个通用时间序列语言模型，可直接从观测时间序列进行预测，基于文本上下文和时间模式进行推理，并整合外部数值预测专家的预测结果。该模型通过共享主干网络独立训练三个低秩专家，分别用于预测聚合、原生预测和时间分析。在GIFT-Eval、Context is Key和TimeSeriesExam等基准测试中，该模型分别以平均MASE排名、RCRPS和准确率排名前三。
OpenTSLM TeeMoE: A Unified Time-Series Language Model for Forecasting, Contextual Prediction, and Reasoning
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.
