Construction of Complex Network with Multiple Time Series Relevance
Multivariate time series data, which comprise a set of ordered observations for multiple variables, are pervasively generated in weather conditions, traffic, financial stocks, etc. Therefore, it is of great significance to analyze the correlation between multiple time series. Financial stocks genera...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-08-01
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| Series: | Information |
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| Online Access: | http://www.mdpi.com/2078-2489/9/8/202 |
| _version_ | 1828918407822573568 |
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| author | Zongwen Huang Lingyu Xu Lei Wang Gaowei Zhang Yaya Liu |
| author_facet | Zongwen Huang Lingyu Xu Lei Wang Gaowei Zhang Yaya Liu |
| author_sort | Zongwen Huang |
| collection | DOAJ |
| description | Multivariate time series data, which comprise a set of ordered observations for multiple variables, are pervasively generated in weather conditions, traffic, financial stocks, etc. Therefore, it is of great significance to analyze the correlation between multiple time series. Financial stocks generate a significant amount of multivariate time series data that can be used to build networks that reflect market behavior. However, traditional commercial complex networks cannot fully utilize the multiple attributes of stocks and redundant filter relationships and reveal a more authentic financial stock market. We propose a fusion similarity of multiple time series and construct a threshold network with similarity. Furthermore, we define the connectivity efficiency to choose the best threshold, establishing a high connectivity efficiency network with the optimal network threshold. By searching the central node in the threshold network, we have found that the network center nodes constructed by our proposed method have a more comprehensive industry coverage than the traditional techniques to build the systems, and this also proves the superiority of this method. |
| first_indexed | 2024-12-13T21:08:31Z |
| format | Article |
| id | doaj.art-0ac9a6d1683642e883f822e7af2dd176 |
| institution | Directory Open Access Journal |
| issn | 2078-2489 |
| language | English |
| last_indexed | 2024-12-13T21:08:31Z |
| publishDate | 2018-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj.art-0ac9a6d1683642e883f822e7af2dd1762022-12-21T23:31:24ZengMDPI AGInformation2078-24892018-08-019820210.3390/info9080202info9080202Construction of Complex Network with Multiple Time Series RelevanceZongwen Huang0Lingyu Xu1Lei Wang2Gaowei Zhang3Yaya Liu4School of Computer Engineering and Science, Shanghai University, Shanghai 200444, ChinaSchool of Computer Engineering and Science, Shanghai University, Shanghai 200444, ChinaEast Sea Information Center, SOA China, Shanghai 644300, ChinaSchool of Computer Engineering and Science, Shanghai University, Shanghai 200444, ChinaSchool of Computer Engineering and Science, Shanghai University, Shanghai 200444, ChinaMultivariate time series data, which comprise a set of ordered observations for multiple variables, are pervasively generated in weather conditions, traffic, financial stocks, etc. Therefore, it is of great significance to analyze the correlation between multiple time series. Financial stocks generate a significant amount of multivariate time series data that can be used to build networks that reflect market behavior. However, traditional commercial complex networks cannot fully utilize the multiple attributes of stocks and redundant filter relationships and reveal a more authentic financial stock market. We propose a fusion similarity of multiple time series and construct a threshold network with similarity. Furthermore, we define the connectivity efficiency to choose the best threshold, establishing a high connectivity efficiency network with the optimal network threshold. By searching the central node in the threshold network, we have found that the network center nodes constructed by our proposed method have a more comprehensive industry coverage than the traditional techniques to build the systems, and this also proves the superiority of this method.http://www.mdpi.com/2078-2489/9/8/202multivariate time seriesfusion similarityconnectivity efficiencythreshold determinationcomplex networkcentral node |
| spellingShingle | Zongwen Huang Lingyu Xu Lei Wang Gaowei Zhang Yaya Liu Construction of Complex Network with Multiple Time Series Relevance Information multivariate time series fusion similarity connectivity efficiency threshold determination complex network central node |
| title | Construction of Complex Network with Multiple Time Series Relevance |
| title_full | Construction of Complex Network with Multiple Time Series Relevance |
| title_fullStr | Construction of Complex Network with Multiple Time Series Relevance |
| title_full_unstemmed | Construction of Complex Network with Multiple Time Series Relevance |
| title_short | Construction of Complex Network with Multiple Time Series Relevance |
| title_sort | construction of complex network with multiple time series relevance |
| topic | multivariate time series fusion similarity connectivity efficiency threshold determination complex network central node |
| url | http://www.mdpi.com/2078-2489/9/8/202 |
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