针对不规则时间序列，提出自适应建模框架提升稀疏数据下的预测性能。
Irregularity-Informed Time Series Analysis: Adaptive Modelling of Spatial and Temporal Dynamics
- 从局部、时间、空间和不规则性四视角构建Transformer模型
- 在88.4%缺失率数据上仍保持稳定性能，显著优于基线方法
- 适合处理医疗、传感器等高缺失场景的时序分析任务
不规则时间序列（IRTS）在现实应用中日益普遍。我们发现IRTS可分为自然不规则（NIRTS）与意外不规则（AIRTS）两类。现有方法或忽略不规则模式，或静态学习其动态，且受限于数据稀疏性。本文提出基于Transformer的通用框架，从局部性、时间、空间和不规则性四个维度建模，最大化数据利用率。设计不规则门控机制，自适应筛选任务相关不规则信息，增强泛化能力。在三个缺失率高达88.4%、94.9%、60%的数据集上验证鲁棒性，并通过消融实验证明不规则信息对NIRTS与AIRTS的重要性。代码已开源：https://github.com/IcurasLW/MTSFormer-Irregular_Time_Series.git。
原文摘要 · Abstract (English)
Irregular Time Series Data (IRTS) has shown increasing prevalence in real-world applications. We observed that IRTS can be divided into two specialized types: Natural Irregular Time Series (NIRTS) and Accidental Irregular Time Series (AIRTS). Various existing methods either ignore the impacts of irregular patterns or statically learn the irregular dynamics of NIRTS and AIRTS data and suffer from limited data availability due to the sparsity of IRTS. We proposed a novel transformer-based framework for general irregular time series data that treats IRTS from four views: Locality, Time, Spatio and Irregularity to motivate the data usage to the highest potential. Moreover, we design a sophisticated irregularity-gate mechanism to adaptively select task-relevant information from irregularity, which improves the generalization ability to various IRTS data. We implement extensive experiments to demonstrate the resistance of our work to three highly missing ratio datasets (88.4\%, 94.9\%, 60\% missing value) and investigate the significance of the irregularity information for both NIRTS and AIRTS by additional ablation study. We release our implementation in https://github.com/IcurasLW/MTSFormer-Irregular_Time_Series.git
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