提出新框架提升跨领域时间序列预测准确率
Unify and Anchor: A Context-Aware Transformer for Cross-Domain Time Series Forecasting
- 分离频率成分并引入外部上下文作为锚点，统一建模视角
- 在多个数据集上超越现有方法，零样本迁移表现优异
- 适合需要跨领域泛化的时序预测场景
基础模型的兴起已彻底改变自然语言处理和计算机视觉领域，但其在时间序列预测中的最佳实践仍待探索。现有时间序列基础模型常照搬其他领域的方法，未考虑时间序列数据的独特性。本文识别出跨领域时间序列预测的两大挑战：时序模式复杂性和语义错位。为此，提出“统一与锚定”迁移范式，通过解耦频率成分实现统一视角，并引入外部上下文作为域锚点以指导适应。基于此框架，提出ContexTST，一种基于Transformer的模型，采用时间序列协调器进行结构化表征，结合上下文感知的专家混合机制的Transformer模块，实现有效的跨域泛化。大量实验表明，ContexTST在多个数据集上均达到先进性能，且具备强大的零样本迁移能力。
原文摘要 · Abstract (English)
The rise of foundation models has revolutionized natural language processing and computer vision, yet their best practices to time series forecasting remains underexplored. Existing time series foundation models often adopt methodologies from these fields without addressing the unique characteristics of time series data. In this paper, we identify two key challenges in cross-domain time series forecasting: the complexity of temporal patterns and semantic misalignment. To tackle these issues, we propose the ``Unify and Anchor" transfer paradigm, which disentangles frequency components for a unified perspective and incorporates external context as domain anchors for guided adaptation. Based on this framework, we introduce ContexTST, a Transformer-based model that employs a time series coordinator for structured representation and the Transformer blocks with a context-informed mixture-of-experts mechanism for effective cross-domain generalization. Extensive experiments demonstrate that ContexTST advances state-of-the-art forecasting performance while achieving strong zero-shot transferability across diverse domains.
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