TimeBraid：统一时间序列与语言的理解和预测模型
TimeBraid: Unifying Time Series and Language for Understanding and Forecasting
这篇论文把语言模型和时间序列模型拼到了一起，用 220 万序列-文本对训练，预测和理解都能做，做时序方向的话可以看看它的对齐设计。
TimeBraid 是一系列统一的时间序列与语言模型，通过交错的全局残差注意力层将预训练语言模型与时间序列基础模型对齐。该模型融合语言侧的知识、指令遵循和推理能力，以及时间序列侧的连续信号感知和零样本预测能力。训练数据包含 2.2M 条精选的序列-文本对和 4.9M 条指令微调样本。在涵盖时间序列感知、理解、推理和预测的多项基准上，TimeBraid 与规模更大的通用模型和任务专用模型相比仍具竞争力。
TimeBraid: Unifying Time Series and Language for Understanding and Forecasting
We present TimeBraid, a series of unified time-series and language models that align pretrained language models and pretrained time-series foundation models through interleaved global residual attention layers. Each model inherits knowledge, instruction following, and reasoning from one side, continuous-signal perception and zero-shot forecasting from the other, and fuses the two in a shared representation space where both modalities are understood and generated. We study the design choices that make such unified modeling work: where to align the two representation spaces, how to ground language in temporal structure, how to balance understanding with generation, and how to keep joint optimization stable. The resulting recipe combines a unified prompting scheme for diverse time-series and text tasks, stabilized joint training, and supervision from 2.2M curated series--text pairs and 4.9M instruction-tuning samples. Across benchmarks spanning time-series perception, understanding, reasoning, and both context-aided and unimodal forecasting, TimeBraid remains competitive with far larger general-purpose models and task-specific counterparts.
