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 the true benefits of some of these architectures are, 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 to account for the new observation, and (ii) time-triggered reset that relies on 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 or 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.

翻译：对不规则采样的多元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存在的挑战。尽管近期研究探索了多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，但这些架构的真正优势仍不明确，并且对更简单、更高效的基于RNN的算法进行巧妙修改是否依然具有竞争力——即与这些方法相当甚至更优——也尚不清楚。在本工作中，我们提出并研究了GRUwE：基于指数基函数的门控循环单元，它建立在用于不规则时间观测的基于RNN的架构之上。GRUwE支持连续时间中基于回归和基于事件的预测。GRUwE通过维护一个随不规则观测到达而更新的时间序列马尔可夫状态表示来工作。马尔可夫状态更新依赖于两种重置机制：(i) 观测触发重置，以考虑新观测；(ii) 时间触发重置，依赖于可学习的指数衰减，以支持连续时间中的预测。我们在多个真实世界基准测试上对下一观测和下一事件预测任务进行的实证评估表明，与近期最先进（SOTA）方法相比，GRUwE确实能够实现具有竞争力甚至更优的性能。得益于其简洁性，GRUwE提供了引人注目的优势：易于实现，需要最少的超参数调整工作，并显著降低了在线部署中的计算开销。