Wasserstein generative adversarial networks for modeling marked events

Marked temporal events are ubiquitous in several areas, where the events’ times and marks (types) are usually interrelated. Point processes and their non-functional variations using recurrent neural networks (RNN) model temporal events using intensity functions. However, since they usually utilize the likelihood maximization approach, they might fail. Moreover, their high simulation complexity makes them inappropriate. Since calculating the intensity function is not always necessary, generative models are utilized for modeling. Generative adversarial networks (GANs) have been successful in modeling point processes, but they still lack in modeling interdependent types and times of events. In this research, a double Wasserstein GAN (WGAN), using a conditional GAN, is proposed which generates types of events that are categorical data, dependent on their times. Experiments on synthetic and real-world data represent that WGAN methods are efficient or competitive with the compared intensity-based models. Furthermore, these methods have a faster simulation than intensity-based methods.