Abstract: The disclosure provides a method for predicting a traffic matrix, a computing device, and a storage medium. The method includes: establishing a dataset based on continuous historical traffic matrices; and inputting one or more historical traffic matrices in the dataset into a trained model for predicting traffic matrices, to obtain one or more predicted traffic matrices. The trained model for predicting traffic matrices is obtained by the following actions: establishing a model for predicting traffic matrices based on a correlation-modeling neural network and a temporal-modeling neural network; and training the model for predicting traffic matrices based on a set of training samples, in which the set of training samples includes sample traffic matrices and label traffic matrices corresponding to the sample traffic matrices at prediction moment samples.

Abstract: The disclosure provides a method for predicting a traffic matrix, a computing device, and a storage medium. The method includes: establishing a dataset based on continuous historical traffic matrices; and inputting one or more historical traffic matrices in the dataset into a trained model for predicting traffic matrices, to obtain one or more predicted traffic matrices. The trained model for predicting traffic matrices is obtained by the following actions: establishing a model for predicting traffic matrices based on a correlation-modeling neural network and a temporal-modeling neural network; and training the model for predicting traffic matrices based on a set of training samples, in which the set of training samples includes sample traffic matrices and label traffic matrices corresponding to the sample traffic matrices at prediction moment samples.