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Time sequence mode representation-based weighted directed complicated network construction method

A time series and complex network technology, applied in the field of complex network construction, can solve problems such as inability to accurately reflect node associations, achieve the effects of improving classification and recognition performance, wide application range, and improving classification or recognition accuracy

Inactive Publication Date: 2017-03-22
TIANJIN UNIV
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  • Abstract
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  • Application Information

AI Technical Summary

Problems solved by technology

It can be seen from this that unweighted and undirected complex networks cannot accurately reflect the association between nodes (or primitives)

Method used

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  • Time sequence mode representation-based weighted directed complicated network construction method
  • Time sequence mode representation-based weighted directed complicated network construction method
  • Time sequence mode representation-based weighted directed complicated network construction method

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Embodiment Construction

[0035] The weighted directed complex network construction method based on time series pattern representation of the present invention will be described in detail below with reference to the embodiments and the accompanying drawings.

[0036] The weighted directed complex network construction method based on time series pattern representation of the present invention comprises the following steps:

[0037] 1) Normalize the original time series using the zero-mean normalization method. The zero-mean normalization method is to convert all time series data into a standardized time series with a mean of 0 and a variance of 1. Through the following calculation formula:

[0038]

[0039]

[0040]

[0041] in, is the mean of the time series, a is the standard deviation of the original time series,

[0042] For the original time series {x i}, i=1,...,t, get a new time series {y after standardization i}, i=1,...,t;

[0043] 2) Put the new time series {y i}, i=1,...,t and ...

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Abstract

A time sequence mode representation-based weighted directed complicated network construction method comprises the steps of adopting a zero-mean normalization method to normalize an original time sequence; dividing a new time sequence into n sections in an equal probability manner, using the characters in a set character string to represent the sections, and representing the new time sequence into a character string sequence; moving a sliding window of which the length is 1 from left to right from the first character of the character string sequence, every time the sliding window moves one step, dividing the character string sequence into ((n-1)+1) fragments of which the lengths are all 1, and regarding each fragment as a mode; taking the different modes as the nodes of a complicated network, determining the connection edge weights and directions between the nodes of the complicated network according to the conversion frequency and the conversion directions between the nodes, and mapping the character string sequence into the weighted directed complicated network; and calculating the network topology statistical characteristics of the weighted directed complicated network. The method of the present invention enables the classification or identification precision of the time sequence signals to be improved remarkably.

Description

technical field [0001] The invention relates to a complex network construction method. In particular, it relates to a weighted directed complex network construction method based on time series pattern representation. Background technique [0002] The complex network analysis method is to abstract the relationship between the internal primitives of the complex system into the form of nodes and edges of the network, and then quantitatively analyze the topological structure and dynamic behavior of the network to reveal important information such as the internal attributes and operating laws of the complex system . The complex network analysis method provides a new perspective for the study of different types of complex systems (such as biological networks, brain neural networks, the World Wide Web, social networks, etc.), so it has received extensive attention from researchers in different disciplines, and has achieved a series of gratifying results. progress. However, with ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04L12/24
CPCH04L41/14
Inventor 曾明赵明愿孟庆浩
Owner TIANJIN UNIV
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