Method and system for training model and method and system for predicting sequence data
A sequence data and sequence prediction technology, which is applied in the field of machine learning model prediction sequence data, can solve the problems of poor model prediction accuracy, inability to handle dynamic graphs and static graphs, etc., and achieve the effect of accurate sequence data prediction results
Active Publication Date: 2022-07-19
THE FOURTH PARADIGM BEIJING TECH CO LTD
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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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[0064] In order for those skilled in the art to better understand the present application, the exemplary embodiments of the present application will be described in further detail below with reference to the accompanying drawings and specific embodiments.
[0065] figure 1 is a block diagram illustrating a system (hereinafter, referred to as a "model training system" for convenience of description) 100 for training a graph convolutional network-based machine learning model for predicting sequence data according to an exemplary embodiment of the present application . like figure 1 As shown, the model training system 100 may include a training sample acquisition device 110 and a training device 120 .
[0066] Specifically, the training sample obtaining means 110 may obtain a sequence training sample set of the object. Here, the sequence training sample set may include a plurality of sequence training samples, and each sequence training sample may include a plurality of sequen...
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Provided are a method and system for training a model and a method and system for predicting sequence data. The method for predicting sequence data includes: obtaining sequence prediction samples of an object, wherein the sequence prediction samples include a plurality of sequence data arranged in time sequence; and using the machine learning model, performing prediction on the sequence prediction samples to provide information about the sequence prediction samples. The prediction result of the next sequence data after the plurality of sequence data, wherein the machine learning model is trained in advance to predict the next sequence after the series of sequence data for a series of sequence data arranged in time sequence data, and the machine learning model includes at least a plurality of graph convolutional networks, wherein the plurality of graph convolutional networks include a first graph convolutional network trained by using dynamic graphs constructed based on the historical sequence data of the object and a second graph convolutional network trained by using the static graph constructed based on the static data related to the object.
Description
technical field [0001] The present application generally relates to the field of artificial intelligence, and more particularly, to a method and system for training a graph convolutional network-based machine learning model for predicting sequence data, and using a graph convolutional network-based machine learning model Methods and systems 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 artificial intelligence development to a certain stage. It is committed to mining valuable potential information from large amounts of data by means of computing. Modeling (eg, chronological traffic flow) through machine learning to mine the patterns behind sequence data is important for various application scenarios (eg, traffic prediction scenarios). [0003] Graph Convolutional Networks (GCNs) are extensions of Convolutional Neural Net...
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IPC IPC(8): G06Q10/04G06Q50/26G06N20/00G06N3/04
CPCG06Q10/04G06Q50/26G06N20/00
Inventor 姚权铭
Owner THE FOURTH PARADIGM BEIJING TECH CO LTD