提出可跨数据集迁移的高维时间序列预测框架，解决通道依赖与扩展性矛盾。
Unicorn: Scaling High-Dimensional Time Series Forecasting via Universal Correlation Modeling
- 用潜在原型码本将不同通道映射到共享空间，解耦相关性建模与通道身份。
- 在少样本迁移场景下显著超越现有模型，支持异构数据集间泛化。
- 适合构建多变量时间序列基础模型的研究者和工业界应用开发者。
现代时间序列模型面临根本性权衡：通道无关模型虽能随数据量扩展，却忽略关键的通道间依赖；而通道相关模型虽表达能力强，但受限于维度，难以跨异构数据集泛化。为此，我们提出UniCorn（通用相关性网络），一种面向高维时间序列的可扩展、多数据集预训练框架。其核心是一个潜在原型码本，将异构通道投影至共享隐空间，实现与通道身份无关的可复用交互模式学习，从而在不同维度与语义的数据域间实现迁移。大量实验表明，UniCorn在少样本迁移场景下显著优于当前最优预测架构，为构建多变量时间序列基础模型提供了可扩展路径。
原文摘要 · Abstract (English)
Modern time series architectures face a fundamental trade-off: channel-independent models scale well with increasing data volume but ignore critical inter-channel dependencies, while channel-dependent models are expressive but remain ``dimension-bounded'', struggling to generalize across heterogeneous datasets.To bridge this gap, we introduce Unicorn (Universal Correlation Network), a framework for scalable, multi-dataset pretraining on high-dimensional time series. At the core of Unicorn is a latent prototype codebook that decouples correlation modeling from specific channel identities. By projecting heterogeneous channels into a shared latent space, UniCorN learns identity-agnostic, reusable interaction patterns that transfer across domains with diverse dimensionalities and semantics. Extensive experiments show that Unicorn significantly outperforms state-of-the-art forecasting architectures, particularly in few-shot transfer scenarios, offering a scalable path toward multivariate time series foundation models.
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