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 and continuous-signal perception and zero-shot forecasting from the other, fusing both in a shared representation space for understanding and generation. We study the design choices enabling unified modeling: where to align the two representation spaces, how to ground language in temporal structure, how to balance understanding with generation, and how to stabilize joint optimization. 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.