Method and system for training model, and method and system for predicting sequence data

Abstract

The invention provides a method and system for training a model, and a method and system for predicting sequence data. The method for predicting sequence data comprises the steps: obtaining a sequenceprediction sample of an object, wherein the sequence prediction sample comprises multiple pieces of sequence data arranged according to the time sequence; and utilizing the machine learning model toperform prediction on the sequence prediction sample to provide a prediction result about next sequence data after the plurality of sequence data, wherein the machine learning model is trained in advance to predict next sequence data after a series of sequence data arranged according to a time sequence, and the machine learning model at least comprises a plurality of graph convolution networks, and the plurality of graph convolution networks comprise a first graph convolution network trained by using a dynamic graph constructed based on historical sequence data of the object and a second graphconvolution network trained by using a static graph constructed based on static data related to the object.

Description

technical field [0001] The present application generally relates to the field of artificial intelligence, and more specifically, relates to a method and system for training a machine learning model based on a graph convolutional network for predicting sequence data, and using a machine learning model based on a graph convolutional network Method and system for predicting sequence data. Background technique [0002] With the emergence of massive data, artificial intelligence technology has developed rapidly, and machine learning is an inevitable product of the development of artificial intelligence to a certain stage. It is committed to mining valuable potential information from large amounts of data through computing means. Modeling (e.g., chronological traffic flow) by machine learning to mine the regularities behind sequence data is very important for various application scenarios (e.g., traffic forecasting scenarios). [0003] Graph Convolutional Networks (GCNs) are exte...

AI Technical Summary

Problems solved by technology

[0008] The present invention is to solve the problem that the current GCN cannot handle the fusion of dynamic graphs and static graphs, resulting in poor model prediction accuracy, for example, in traffic prediction scenarios, to improve the accuracy of traffic flow prediction

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