用时间嵌入捕捉不规则自报数据，提升长期压力预测准确率
Learning Longitudinal Stress Dynamics from Irregular Self-Reports via Time Embeddings
- 设计Ema2Vec时间嵌入方法，处理不规则采样自报数据
- 在长期压力预测任务中超越固定窗口基线模型
- 适合做心理状态建模与可穿戴设备研究的学者参考
移动与可穿戴传感技术的广泛应用使得对情绪、心境障碍和压力的持续个性化监测成为可能。结合生态瞬时评估（EMA）自报问卷，这些系统为探索人类行为的长期建模提供了强大机会。然而，缺失数据与自报时间的不规则性给人类状态与行为预测带来了挑战。本研究探讨了利用时间嵌入捕捉EMA序列中的时间依赖性。我们提出一种新型时间嵌入方法Ema2Vec，专为处理不规则间隔的自报数据而设计，并在新的长期压力预测任务上进行评估。结果表明，该方法优于依赖固定大小日窗口的标准基线模型，也优于未使用时间感知表示的纵向序列直接建模方法。这些发现强调了在建模不规则采样纵向数据时引入时间嵌入的重要性。
原文摘要 · Abstract (English)
The widespread adoption of mobile and wearable sensing technologies has enabled continuous and personalized monitoring of affect, mood disorders, and stress. When combined with ecological self-report questionnaires, these systems offer a powerful opportunity to explore longitudinal modeling of human behaviors. However, challenges arise from missing data and the irregular timing of self-reports, which make challenging the prediction of human states and behaviors. In this study, we investigate the use of time embeddings to capture time dependencies within sequences of Ecological Momentary Assessments (EMA). We introduce a novel time embedding method, Ema2Vec, designed to effectively handle irregularly spaced self-reports, and evaluate it on a new task of longitudinal stress prediction. Our method outperforms standard stress prediction baselines that rely on fixed-size daily windows, as well as models trained directly on longitudinal sequences without time-aware representations. These findings emphasize the importance of incorporating time embeddings when modeling irregularly sampled longitudinal data.
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