用可学习查询令牌直接嵌入不规则时间序列，无需插值或改架构。
QuITE: Query-Based Irregular Time Series Embedding
- 用可学习查询令牌通过自注意力聚合不规则数据点。
- 预测任务最高提升54.7%，分类任务提升15.8%。
- 即插即用，适配多种模型与数据集，无需修改主干结构。
不规则多变量时间序列（IMTS）在实际中常见，但其采样不规则给建模带来挑战。现有方法要么设计专用架构，限制已有模型复用；要么通过插值映射到规则网格，可能扭曲时间动态并引入虚假值。本文指出关键瓶颈不在主干架构，而在传统嵌入层对均匀采样的假设。为此，提出QuITE（基于查询的不规则时间序列嵌入），一种简单有效的即插即用嵌入模块。QuITE利用可学习查询令牌，通过单一自注意力层聚合不规则观测，直接生成主干兼容的潜在表示，无需人工构造数值或修改架构。在真实世界基准上的大量实验表明，QuITE持续提升多种MTS模型性能，在预测任务上平均相对增益达54.7%，分类任务达15.8%。代码已开源。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。