Deep learning models used to predict spatio-temporal data usually make use of embeddings to represent the different nodes that make up a graph, and thus are able to represent the characteristics of the nodes to be predicted. While in other fields of deep learning, such as NLP, a pre-training is performed on large datasets to obtain the embeddings and then apply them to another task with a smaller dataset, in the case of spatio-temporal problems, this is a more complex task. Therefore, in this paper, we propose a method for training on several graphs simultaneously to improve embeddings, using a model adapted to the problem and a dataset generated from subgraphs. To validate the method, a new dataset has been generated from several datasets used for traffic forecasting. The results obtained show that embeddings generated with training on multiple datasets increase prediction accuracy, improving metrics in the datasets used for validation. In addition, an analysis of the embeddings has been performed to add explainability to our method, providing a better understanding of how this training affects the generated embeddings.
García-Sigüenza et al. (Sat,) studied this question.