Rethinking Universal Time Series Foundation Models from Unified Representations to Shared Dynamics (UniTFU)

Time series foundation models (TSFMs) have attracted increasing attention in cross-domain forecasting. Existing works mainly rely on large-scale pre-training and general modeling paradigms. However, the former heavily relies on the diversity and domain coverage of pre-training data. Meanwhile, the latter learns unified representations across heterogeneous domains without explicitly identifying the temporal regularities shared among them, limiting robust cross-domain generalization. This work explores cross-domain generalization by characterizing commonalities in recurring local dynamics across domains, rather than relying solely on implicitly learned temporal representations. To capture such cross-domain commonalities, we propose **Time Series Foundation Units (TFUs)** as reusable building blocks discovered from multi-scale temporal segments, each characterizing a recurring local dynamic pattern shared across domains. Building on TFUs, we develop a unified time series foundation model (**UniTFU**) that explicitly reuses and composes shared temporal regularities across domains for forecasting. Experiments on seven public datasets show that UniTFU substantially outperforms existing TSFMs in cross-domain forecasting and can efficiently adapt to target-domain dynamics by updating its reusable components with limited target data. UniTFU also achieves strong forecasting performance compared with state-of-the-art methods.