This paper presents a novel approach for modeling and predicting patterns of events in time-series learning, named graph recurrent temporal point process (GRTPP). Prior research has focused on using deep learning techniques, such as recurrent neural networks (RNNs) or attention-based sequential data embedding, on modeling the time-varying intensity of events. However, these models were typically limited to modeling a single intensity function capturing the event occurrence of all event types simultaneously. GRTPP addresses this issue by encoding multivariate event sequences into a sequence of graphs, where each node contains information about the event occurrence and time. The sequence of graphs is then embedded into node embeddings for each event type, taking into account the relationships between the event types. By integrating the estimated intensity functions, GRTPP predicts the event type and the timing of the next event. The proposed GRTPP model offers improved effectiveness and explainability compared to previous models, as demonstrated through empirical evaluations on five real-world datasets and the actual credit card transaction dataset. The code is available at https://github.com/im0j/GRTPP https://github.com/im0j/GRTPP.