EMIT：针对不规则时间序列的基于事件的掩码自编码
EMIT- Event-Based Masked Auto Encoding for Irregular Time Series
AI总结：
本文提出针对不规则时间序列的预训练框架EMIT，通过基于数据变化率的事件掩码在潜在空间进行重建，有效保留数据变异性和时间信息，在MIMIC-III等数据集上表现优越。
摘要：
不规则时间序列，即数据点在不均匀的间隔下被记录，在医疗环境中普遍存在，例如在急诊病房中，生命体征和实验室结果会在不同时间被采集。这种反映了患者健康状况关键波动的变异性，对于知情的临床决策至关重要。现有针对不规则时间序列的自监督学习研究通常依赖于预测等通用前置任务，这可能无法充分利用不规则时间序列提供的信号。为了提升模型性能和鲁棒性，特别是在数据可用性受限的场景下，迫切需要专门针对不规则时间序列特性设计的专用前置任务。本文提出了一种新颖的预训练框架EMIT，一种针对不规则时间序列的基于事件的掩码方法。EMIT专注于在潜在空间中进行基于掩码的重建，根据数据的变化率来选择掩码点。该方法保留了测量的自然变异性和时间，同时增强了模型处理不规则间隔的能力，且不丢失基本信息。在MIMIC-III和PhysioNet Challenge数据集上的大量实验证明了我们基于事件的掩码策略的优越性能。代码已在https://github.com/hrishi-ds/EMIT发布。
英文摘要：
Irregular time series, where data points are recorded at uneven intervals, are prevalent in healthcare settings, such as emergency wards where vital signs and laboratory results are captured at varying times. This variability, which reflects critical fluctuations in patient health, is essential for informed clinical decision-making. Existing self-supervised learning research on irregular time series often relies on generic pretext tasks like forecasting, which may not fully utilise the signal provided by irregular time series. There is a significant need for specialised pretext tasks designed for the characteristics of irregular time series to enhance model performance and robustness, especially in scenarios with limited data availability. This paper proposes a novel pretraining framework, EMIT, an event-based masking for irregular time series. EMIT focuses on masking-based reconstruction in the latent space, selecting masking points based on the rate of change in the data. This method preserves the natural variability and timing of measurements while enhancing the model's ability to process irregular intervals without losing essential information. Extensive experiments on the MIMIC-III and PhysioNet Challenge datasets demonstrate the superior performance of our event-based masking strategy. The code has been released at https://github.com/hrishi-ds/EMIT.
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。
