标题： 仍然具有竞争力：重新审视递归模型在不规则时间序列预测中的应用 显示英文标题

标题： Still Competitive: Revisiting Recurrent Models for Irregular Time Series Prediction

摘要： 对不规则采样多变量时间序列进行建模是医疗保健和传感器网络等领域中一个持续的挑战。 尽管最近的研究探索了各种复杂的学习架构来解决不规则采样时间序列的预测问题，但这些架构的真实优势尚不清楚，而且简单的更高效的基于RNN的算法的巧妙修改是否仍然具有竞争力，即它们是否与这些方法相当甚至更优，也尚未明确。 在本工作中，我们提出并研究了GRUwE：带有指数基函数的门控循环单元，该模型基于基于RNN的架构，用于在不规则时间点进行观测。 GRUwE支持连续时间中的基于回归的预测和基于事件的预测。 GRUwE通过维护一个马尔可夫状态表示来工作，该表示随着不规则观测的到来而更新。 马尔可夫状态更新依赖于两个重置机制：(i) 观测触发的重置，以及(ii) 使用可学习的指数衰减对GRU状态进行时间触发的重置，以支持连续时间的预测。 我们在多个现实世界基准数据集上对下一个观测和下一个事件预测任务进行了实证评估，结果表明GRUwE确实可以实现与最近的最先进（SOTA）方法相当或更优的性能。 由于其简单性，GRUwE提供了有吸引力的优势：它易于实现，需要最少的超参数调整努力，并显著减少了在线部署中的计算开销。 显示英文摘要

摘要： Modeling irregularly sampled multivariate time series is a persistent challenge in domains like healthcare and sensor networks. While recent works have explored a variety of complex learning architectures to solve the prediction problems for irregularly sampled time series, it remains unclear what are the true benefits of some of these architectures, and whether clever modifications of simpler and more efficient RNN-based algorithms are still competitive, i.e. they are on par with or even superior to these methods. In this work, we propose and study GRUwE: Gated Recurrent Unit with Exponential basis functions, that builds upon RNN-based architectures for observations made at irregular times. GRUwE supports both regression-based and event-based predictions in continuous time. GRUwE works by maintaining a Markov state representation of the time series that updates with the arrival of irregular observations. The Markov state update relies on two reset mechanisms: (i) observation-triggered reset, and (ii) time-triggered reset of the GRU state using learnable exponential decays, to support the predictions in continuous time. Our empirical evaluations across several real-world benchmarks on next-observation and next-event prediction tasks demonstrate that GRUwE can indeed achieve competitive to superior performance compared to the recent state-of-the-art (SOTA) methods. Thanks to its simplicity, GRUwE offers compelling advantages: it is easy to implement, requires minimal hyper-parameter tuning efforts, and significantly reduces the computational overhead in the online deployment.