让时间序列大模型能统一处理各类外部数据，提升实际预测能力。
UniCA: Unified Covariate Adaptation for Time Series Foundation Model
- 将异构外部数据转为统一表示，再用注意力机制融合
- 在多类任务上显著优于现有方法，支持图文等多元输入
- 适合需要结合复杂外部信息的工业级时序预测场景
时间序列基础模型（TSFMs）通过大规模预训练取得了显著进展，但其设计主要针对实值序列，难以有效处理包含多样化异构协变量（如类别变量、多模态数据）的通用预测任务。为此，我们提出统一协变量适配框架UniCA，先对异构协变量进行同质化处理，生成高层次统一表示，再通过统一的注意力融合机制整合。UniCA兼容同质与异质协变量，可在不损害TSFM泛化能力的前提下引入额外协变量信息。在多个单模态与多模态协变量感知预测基准上的大量实验表明，UniCA性能优越，凸显了协变量感知型TSFM适配在真实场景中的潜力。代码已开源。
原文摘要 · Abstract (English)
Time Series Foundation Models (TSFMs) have achieved remarkable success through large-scale pretraining. However, their design primarily targets real-valued series, limiting their ability to handle general forecasting tasks involving diverse and often heterogeneous covariates -- such as categorical variables and multimodal data (e.g., images, text) -- which are typically task-specific and difficult to leverage during pretraining. To address this gap, we propose Unified Covariate Adaptation (UniCA), a framework to bridge TSFMs with general covariate-aware forecasting. UniCA first performs covariate homogenization to transform heterogeneous covariates into high-level homogeneous series representations and then fuses them via a unified attention-based fusion mechanism. UniCA is compatible and universal for adaptation with both homogeneous and heterogeneous covariates, incorporating extra covariate information while preserving the generalization ability of TSFMs.Extensive experiments on multiple unimodal and multimodal covariate-aware forecasting benchmarks demonstrate the superiority of UniCA, highlighting the promise of covariate-aware TSFM adaptation in real-world forecasting scenarios.Code: https://github.com/hanlu-nju/UniCA.
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