Temporal or spatio-temporal sequential pattern discovery is a well-recognized important problem in many domains like seismology, criminology, and finance. The majority of the current approaches are based on candidate generation which necessitates parameter tuning, namely, definition of a neighborhood, an interest measure, and a threshold value to evaluate candidates. However, their performance is limited as the success of these methods relies heavily on parameter settings. In this paper, we propose an algorithm which uses a nonparametric stochastic de-clustering procedure and a multivariate Hawkes model to define triggering relations within and among the event types and employs the estimated model to extract significant triggering patterns of event types. We tested the proposed method with real and synthetic data sets exhibiting different characteristics. The method gives good results that are comparable with the methods based on candidate generation in the literature.