To analyze multivariate time series, most previous methods assume regular subsampling of time series, where the interval between adjacent measurements and the number of samples remain unchanged. Practically, data collection systems could produce irregularly sampled time series due to sensor failures and interventions. However, existing methods designed for regularly sampled multivariate time series cannot directly handle irregularity owing to misalignment along both temporal and variate dimensions. To fill this gap, we propose Compatible Transformer (CoFormer), a transformer-based encoder to achieve comprehensive temporal-interaction feature learning for each individual sample in irregular multivariate time series. In CoFormer, we view each sample as a unique variate-time point and leverage intra-variate/inter-variate attentions to learn sample-wise temporal/interaction features based on intra-variate/inter-variate neighbors. With CoFormer as the core, we can analyze irregularly sampled multivariate time series for many downstream tasks, including classification and prediction. We conduct extensive experiments on 3 real-world datasets and validate that the proposed CoFormer significantly and consistently outperforms existing methods.

翻译：为分析多变量时间序列，以往多数方法假设时间序列采用规则子采样，即相邻测量点之间的间隔与样本数量保持不变。然而实际应用中，数据采集系统可能因传感器故障或干预而产生不规则采样的时间序列。现有针对规则采样多变量时间序列设计的方法，由于时间维度和变量维度的错位，无法直接处理不规则性。为解决这一问题，我们提出兼容Transformer（CoFormer）——一种基于Transformer的编码器，旨在实现对不规则多变量时间序列中每个独立样本的全面时序-交互特征学习。在CoFormer中，我们将每个样本视为唯一的变量-时间点，并利用变量内/变量间注意力机制，基于变量内/变量间邻居学习样本级的时序/交互特征。以CoFormer为核心，可对不规则采样的多变量时间序列进行分类、预测等多种下游任务分析。在三个真实数据集上的广泛实验表明，所提出的CoFormer显著且稳定地优于现有方法。