Spatio-temporal data prediction method based on graph convolution network

Abstract

The invention discloses a spatio-temporal data prediction method based on a graph convolution network. The method comprises the following steps: obtaining spatio-temporal data as an object of a prediction task; processing the obtained spatio-temporal data to obtain a data set; constructing a spatio-temporal data prediction model based on a graph convolution network; taking the obtained training sample and the verification sample as inputs for constructing a model, executing a training algorithm to obtain model parameters, and determining a spatio-temporal data model based on a graph convolutional network; and inputting time and space to be measured, and executing the spatial-temporal data model based on the graph convolutional network, thereby obtaining an expected prediction result. The method can effectively capture the time attribute and spatial structure features in the spatio-temporal data, thereby improving the accuracy of a spatio-temporal data prediction task, and has very important application values in various fields of disease monitoring, traffic management and the like.

Description

technical field [0001] The invention relates to the technical field of data mining, in particular to a spatio-temporal data prediction method based on a graph convolutional network. Background technique [0002] In the real world, there are many data that have both time attributes and spatial characteristics, such as meteorological monitoring data, traffic monitoring data, regional disaster data, etc. This type of data is called spatio-temporal data. The specificity of spatio-temporal data is that it will not only change in the time dimension, but also in the space dimension. Taking traffic monitoring data as an example, the traffic flow of a certain monitoring point will change with time, and the traffic flow of adjacent monitoring points will affect each other, and the degree of influence will also change with time. [0003] With the popularity of the Internet and sensors, spatiotemporal data has become a typical data type in the era of big data, and spatiotemporal data m...

AI Technical Summary

Problems solved by technology

[0004] Existing spatio-temporal data mining methods either use long short-term memory network (LSTM) to predict spatio-temporal data as time series data, ignoring the

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