探究一种模型无关且无需插值的不规则采样多变量时间序列建模方法
Investigating a Model-Agnostic and Imputation-Free Approach for Irregularly-Sampled Multivariate Time-Series Modeling
AI总结：
本文提出一种无需插值的模型无关方法MissTSM，用于不规则采样多变量时间序列建模，在高缺失率和无周期结构条件下表现优异。
摘要：
对不规则采样和多变量时间序列（IMTS）建模至关重要，因为在各种应用中，由于传感器故障或高数据采集成本，不同变量集可能在不同时间步缺失。现有的IMTS方法要么考虑先插值再建模的两阶段框架，要么涉及专门针对特定模型和任务的架构。我们进行了一系列实验，以在各种半合成和真实世界数据集上获取IMTS方法性能的新的见解，适用于分类和预测。我们还引入了缺失特征感知时间序列建模（MissTSM）或MissTSM，一种新的模型无关且无需插值的IMTS建模方法。我们显示，MissTSM在缺失值数量较大且数据缺乏简单周期结构的情况下表现具有竞争力，这在真实世界IMTS应用中很常见。
英文摘要：
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.
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。
