Network coupling time sequence information flow prediction method based on causal logic and graph convolution feature extraction

A feature extraction and network coupling technology, applied in prediction, neural learning methods, biological neural network models, etc.

Pending Publication Date: 2021-02-09
SOUTHEAST UNIV
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Problems solved by technology

[0006] The purpose of the present invention is to address the above-mentioned problems existing in the existing technology, and to provide a network coupling time-series information flow prediction method based on causal logic and graph convolution feature extraction. The technical problem to be solved by the present invention is to calculate the The transfer entropy among them establi

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  • Network coupling time sequence information flow prediction method based on causal logic and graph convolution feature extraction
  • Network coupling time sequence information flow prediction method based on causal logic and graph convolution feature extraction
  • Network coupling time sequence information flow prediction method based on causal logic and graph convolution feature extraction

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Embodiment 1

[0058] Embodiment 1: see figure 1 — Figure 5 , a network coupling temporal information flow prediction method based on causal logic and graph convolution feature extraction, the method comprising the following steps:

[0059] S1, based on the time series data of N sampling nodes, use transfer entropy (Transferentropy) to establish a causal logic network G=(V,E);

[0060] Transfer entropy is a measure of the dependence between two random events X and Y in information theory, the expression is:

[0061]

[0062] where entropy H X =-∑ x p(x) log 2 p(x) measures the uncertainty of a discrete random variable X under the probability distribution p(x), is the k-order time-delayed subsequence of the known process X The conditional entropy when is the k-order time-delay subsequence of the known process X and the l-order time-delay subsequence of the process Y Conditional entropy at time. If TE Y→X >0, there is information flow between Y and X.

[0063] A causal log...

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Abstract

The invention discloses a network coupling time sequence information flow prediction method based on causal logic and graph convolution feature extraction. The method comprises the following steps: establishing a causal logic network by using transfer entropy based on node time sequence data, performing feature extraction on logic network node data by using a graph convolutional network (GCN), performing flow prediction by using a gated cycle unit (GRU) on the basis of a graph information feature h, and performing training optimization on parameters by using a back propagation algorithm. By adding the logic information of the causal network and combining the graph convolution network and the gating cycle unit, the information flow prediction precision is improved.

Description

technical field [0001] The invention relates to a network coupling time series information flow prediction method based on causal logic and graph convolution feature extraction, which belongs to the technical field of information flow prediction. Background technique [0002] Modern management services have entered a new era driven by data resources and information technology. Big data provides a broader perspective and a more effective approach for information management and services. Relying on the "data intelligence" formed by multi-source big data resources, it is urgent to build a development model guided by the big data system and driven by technological innovation to meet the needs of modern information management services. First of all, it is necessary to improve the networked and standardized data state perception collection system and further integrate data resources; second, it is necessary to innovate big data analysis applications to support efficient operation ...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06N3/04G06N3/08
CPCG06Q10/04G06N3/049G06N3/084G06N3/045
Inventor 虞文武
Owner SOUTHEAST UNIV
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