QuITE: 基于查询的不规则时间序列嵌入
QuITE: Query-Based Irregular Time Series Embedding
AI总结：
提出一种即插即用的嵌入模块QuITE，通过可学习查询令牌聚合不规则观测值，无需插值或修改架构，显著提升多变量时间序列模型的预测和分类性能。
摘要：
不规则多变量时间序列在实践中很常见，但其不规则采样给有效建模带来了困难。现有方法通常要么(i)设计专门架构，限制了经过验证的多变量时间序列模型的复用，要么(ii)通过插值将不规则时间序列映射到规则时间网格，这可能会引入人工值从而扭曲时间动态。为解决这些限制，我们提出了一种新的基于输入嵌入的方法。我们发现关键瓶颈不在于主干架构，而在于假设均匀采样的传统嵌入层。在这项工作中，我们引入了QuITE（基于查询的不规则时间序列嵌入），一种简单而有效的即插即用嵌入模块。QuITE使用可学习查询令牌通过单层自注意力聚合不规则观测值，直接生成与主干兼容的潜在表示，无需生成人工值或修改架构。在真实世界基准上的大量实验表明，QuITE持续改进多变量时间序列模型，在不同数据集和主干架构上，预测任务平均相对提升高达54.7%，分类任务平均相对提升高达15.8%。代码可在 https://github.com/Meaningfull9502/QuITE 获取。
英文摘要：
Irregular Multivariate Time Series (IMTS) are common in practice, yet their irregular sampling complicates effective modeling. Existing approaches typically either (i) design specialized architectures that limit the reuse of proven Multivariate Time Series (MTS) models, or (ii) map IMTS onto regular temporal grids through interpolation, which may distort temporal dynamics by introducing artificial values. To address these limitations, we propose a new input-embedding-based approach. We identify that the key bottleneck lies not in the backbone architecture, but in conventional embedding layers that assume uniform sampling. In this work, we introduce QuITE (Query-Based Irregular Time Series Embedding), a simple yet effective plug-and-play embedding module for IMTS. QuITE employs learnable query tokens to aggregate irregular observations through a single self-attention layer, directly producing backbone-compatible latent representations without artificial value generation or architectural modification. Extensive experiments on real-world benchmarks show that QuITE consistently improves MTS models, yielding average relative gains of up to $54.7\%$ in forecasting and $15.8\%$ in classification across diverse datasets and backbone architectures. Code is available at: https://github.com/Meaningfull9502/QuITE.
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。
