Marked Temporal Dynamics Modeling based on Recurrent Neural Network
This addresses the challenge of accurately predicting event times and types in event stream data, which is incremental as it builds on existing methods by integrating predictions.
The paper tackles the problem of modeling and predicting marked temporal dynamics, where events have both time and type information, by proposing a method that simultaneously predicts both using a mark-specific intensity function, and it demonstrates superior performance over state-of-the-art methods in experiments on two datasets.
We are now witnessing the increasing availability of event stream data, i.e., a sequence of events with each event typically being denoted by the time it occurs and its mark information (e.g., event type). A fundamental problem is to model and predict such kind of marked temporal dynamics, i.e., when the next event will take place and what its mark will be. Existing methods either predict only the mark or the time of the next event, or predict both of them, yet separately. Indeed, in marked temporal dynamics, the time and the mark of the next event are highly dependent on each other, requiring a method that could simultaneously predict both of them. To tackle this problem, in this paper, we propose to model marked temporal dynamics by using a mark-specific intensity function to explicitly capture the dependency between the mark and the time of the next event. Extensive experiments on two datasets demonstrate that the proposed method outperforms state-of-the-art methods at predicting marked temporal dynamics.