Modelling event sequence data by type-wise neural point process

Event sequence data widely exists in real life, where each event is typically represented as a tuple, event type and occurrence time. Recently, neural point process (NPP), a probabilistic model that learns the next event distribution with events history given, has gained a lot of attention for event sequence modelling. Existing NPP models use one single vector to encode the whole events history. However, each type of event has its own historical events of concern, which should have led to a different encoding for events history. To this end, we propose Type-wise Neural Point Process (TNPP), with each type of event having a history vector to encode the historical events of its own interest. Type-wise encoding further leads to the realization of type-wise decoding, which together makes a more effective neural point process. Experimental results on six datasets show that TNPP outperforms existing models on the event type prediction task under both extrapolation and interpolation setting. Moreover, the results in terms of scalability and interpretability show that TNPP scales well to datasets with many event types and can provide high-quality event dependencies for interpretation. The code and data can be found at https://github.com/lbq8942/TNPP.