Imputing Missing Events in Continuous-Time Event Streams
Imputing Missing Events in Continuous-Time Event Streams
复制标题
DOI：
--
复制
发表时间：
2019-05
期刊：
影响因子：
--
通讯作者：
Hongyuan Mei;Guanghui Qin;Jason Eisner
中科院分区：
文献类型：
--
作者：
Hongyuan Mei;Guanghui Qin;Jason Eisner
世界上的事件可能是由其他未观察到的事件引起的。我们考虑连续时间中的事件序列。给出了一个完整序列的概率模型，我们提出了粒子平滑-一种序列重要性抽样的形式-来估算不完整序列中的缺失事件。我们基于一种双向连续时间LSTM开发了一个可训练的建议分布族：双向性让建议以未来的观察为条件，而不仅仅是像粒子滤波那样以过去为条件。我们的方法可以对可能的完整序列（粒子）的集合进行采样，从中我们形成一个单一的共识预测，该预测在我们选择的损失度量下具有低贝叶斯风险。我们在多个合成和真实的域中进行实验，使用不同的缺失机制，并使用神经Hawkes过程对每个域中的完整序列进行建模（Mei & Reynner 2017）。在不完整的序列上，我们的方法可以有效地推断出未观察到的地面实况事件，粒子平滑不断提高粒子滤波。
Events in the world may be caused by other, unobserved events. We consider sequences of events in continuous time. Given a probability model of complete sequences, we propose particle smoothing---a form of sequential importance sampling---to impute the missing events in an incomplete sequence. We develop a trainable family of proposal distributions based on a type of bidirectional continuous-time LSTM: Bidirectionality lets the proposals condition on future observations, not just on the past as in particle filtering. Our method can sample an ensemble of possible complete sequences (particles), from which we form a single consensus prediction that has low Bayes risk under our chosen loss metric. We experiment in multiple synthetic and real domains, using different missingness mechanisms, and modeling the complete sequences in each domain with a neural Hawkes process (Mei & Eisner 2017). On held-out incomplete sequences, our method is effective at inferring the ground-truth unobserved events, with particle smoothing consistently improving upon particle filtering.