Massive samples of event sequences data occur in various domains, including e-commerce, healthcare, and finance. There are two main challenges regarding inference of such data: computational and methodological. The amount of available data and the length of event sequences per client are typically large, thus it requires long-term modelling. Moreover, this data is often sparse and non-uniform, making classic approaches for time series processing inapplicable. Existing solutions include recurrent and transformer architectures in such cases. To allow continuous time, the authors introduce specific parametric intensity functions defined at each moment on top of existing models. Due to the parametric nature, these intensities represent only a limited class of event sequences. We propose the COTIC method based on a continuous convolution neural network suitable for non-uniform occurrence of events in time. In COTIC, dilations and multi-layer architecture efficiently handle dependencies between events. Furthermore, the model provides general intensity dynamics in continuous time - including self-excitement encountered in practice. The COTIC model outperforms existing approaches on majority of the considered datasets, producing embeddings for an event sequence that can be used to solve downstream tasks - e.g. predicting next event type and return time. The code of the proposed method can be found in the GitHub repository (https://github.com/VladislavZh/COTIC).

翻译：事件序列数据的大规模样本出现在包括电子商务、医疗和金融等多个领域。对于此类数据的推断存在两个主要挑战：计算层面和方法论层面。每个客户端可用数据量及事件序列的长度通常较大，因此需要长期建模。此外，这类数据往往稀疏且非均匀，使得经典的时间序列处理方法不再适用。现有解决方案在此类情况下包括循环架构和Transformer架构。为了实现连续时间，研究者需在现有模型之上引入每个时刻定义的特定参数化强度函数。由于参数化特性，这些强度仅能表示有限类别的事件序列。我们提出了一种基于连续卷积神经网络的方法COTIC，适用于时间上非均匀发生的事件。在COTIC中，膨胀与多层架构能高效处理事件间的依赖关系。此外，该模型可提供连续时间下的一般性强度动态特性——包括实践中常见的自激发行为。在大多数所考虑的数据集上，COTIC模型优于现有方法，并能为事件序列生成可用于解决下游任务的嵌入表示（例如预测下一事件类型及返回时间）。所提方法的代码可在GitHub仓库中找到（https://github.com/VladislavZh/COTIC）。