多变量时间序列预测中的通道级检索
Channel-wise Retrieval for Multivariate Time Series Forecasting
AI总结：
本文提出CRAFT框架，通过通道级检索增强预测，利用时域稀疏关系图和频域谱相似性提升效率，实验证明在七个基准数据集上优于现有方法。
摘要：
多变量时间序列预测常因固定回顾窗口难以捕捉长程依赖。检索增强预测通过从记忆中检索历史片段解决此问题，但现有方法采用无通道区别的策略，忽视了不同通道间的异质性，即不同通道表现出不同的周期性和谱特征。我们提出CRAFT（通道级检索增强预测），一种新的框架，对每个通道独立进行检索。为确保效率，CRAFT采用两阶段流程：在时域构建稀疏关系图以剔除无关候选，频域中的谱相似性对参考片段进行排序，强调主导周期成分并抑制噪声。在七个公开基准数据集上的实验表明，CRAFT优于现有最先进预测基线，实现了更高的准确性与实用的推理效率。
英文摘要：
Multivariate time series forecasting often struggles to capture long-range dependencies due to fixed lookback windows. Retrieval-augmented forecasting addresses this by retrieving historical segments from memory, but existing approaches rely on a channel-agnostic strategy that applies the same references to all variables. This neglects inter-variable heterogeneity, where different channels exhibit distinct periodicities and spectral profiles. We propose CRAFT (Channel-wise retrieval-augmented forecasting), a novel framework that performs retrieval independently for each channel. To ensure efficiency, CRAFT adopts a two-stage pipeline: a sparse relation graph constructed in the time domain prunes irrelevant candidates, and spectral similarity in the frequency domain ranks references, emphasizing dominant periodic components while suppressing noise. Experiments on seven public benchmarks demonstrate that CRAFT outperforms state-of-the-art forecasting baselines, achieving superior accuracy with practical inference efficiency.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
