提出无需补全缺失值的通用时序建模方法，适合真实世界复杂缺损数据。
Investigating a Model-Agnostic and Imputation-Free Approach for Irregularly-Sampled Multivariate Time-Series Modeling
- 不依赖补全，直接建模带缺失的多变量时序数据
- 在高缺失率且无周期性的数据上表现优于现有方法
- 适用于传感器故障或采样成本高的真实场景
不规则采样多变量时间序列（IMTS）建模在多种应用中至关重要，因传感器故障或采样成本高，不同变量在不同时间步可能缺失。现有方法多采用先补全再建模的两阶段框架，或依赖特定模型和任务的专用架构。我们通过一系列实验，在多种半合成与真实数据集上评估了不同IMTS方法在分类与预测任务中的表现。本文提出一种新型、模型无关且无需补全的建模方法——缺失特征感知时间序列建模（MissTSM）。结果表明，当缺失值较多且数据缺乏简单周期结构时，MissTSM性能显著优于其他方法，这类条件在真实世界IMTS中十分常见。
原文摘要 · Abstract (English)
Modeling Irregularly-sampled and Multivariate Time Series (IMTS) is crucial across a variety of applications where different sets of variates may be missing at different time-steps due to sensor malfunctions or high data acquisition costs. Existing approaches for IMTS either consider a two-stage impute-then-model framework or involve specialized architectures specific to a particular model and task. We perform a series of experiments to derive novel insights about the performance of IMTS methods on a variety of semi-synthetic and real-world datasets for both classification and forecasting. We also introduce Missing Feature-aware Time Series Modeling (MissTSM) or MissTSM, a novel model-agnostic and imputation-free approach for IMTS modeling. We show that MissTSM shows competitive performance compared to other IMTS approaches, especially when the amount of missing values is large and the data lacks simplistic periodic structures - conditions common to real-world IMTS applications.
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。