A novel time-frequency multilayer network for multivariate time series analysis
Unveiling complex dynamics of natural systems from a multivariate time series represents a research hotspot in a broad variety of areas. We develop a novel multilayer network analysis framework, i.e. multivariate time-frequency multilayer network (MTFM network), to peer into the complex system dynam...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2018-01-01
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| Series: | New Journal of Physics |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/1367-2630/aaf51c |
| _version_ | 1797750243999088640 |
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| author | Weidong Dang Zhongke Gao Dongmei Lv Mingxu Liu Qing Cai Xiaolin Hong |
| author_facet | Weidong Dang Zhongke Gao Dongmei Lv Mingxu Liu Qing Cai Xiaolin Hong |
| author_sort | Weidong Dang |
| collection | DOAJ |
| description | Unveiling complex dynamics of natural systems from a multivariate time series represents a research hotspot in a broad variety of areas. We develop a novel multilayer network analysis framework, i.e. multivariate time-frequency multilayer network (MTFM network), to peer into the complex system dynamics. Through mapping the system features into different frequency-based layers and inferring interactions (edges) among different channels (nodes), the MTFM network allows efficiently integrating time, frequency and spatial information hidden in a multivariate time series. We employ two dynamic systems to illustrate the effectiveness of the MTFM network. We first apply the MTFM network to analyze the 48-channel measurements from industrial oil–water flows and reveal the complex dynamics ruling the transition of different flow patterns. The MTFM network is then utilized to analyze 30-channel fatigue driving electroencephalogram signals. The results demonstrate that MTFM network enables to quantitatively characterize brain behavior associated with fatigue driving. Our MTFM network enriches the multivariate time series analysis theory and helps to better understand the complicated dynamical behaviors underlying complex systems. |
| first_indexed | 2024-03-12T16:29:49Z |
| format | Article |
| id | doaj.art-cd541a7abaa7405e872789976ead253e |
| institution | Directory Open Access Journal |
| issn | 1367-2630 |
| language | English |
| last_indexed | 2024-03-12T16:29:49Z |
| publishDate | 2018-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | New Journal of Physics |
| spelling | doaj.art-cd541a7abaa7405e872789976ead253e2023-08-08T15:33:35ZengIOP PublishingNew Journal of Physics1367-26302018-01-01201212500510.1088/1367-2630/aaf51cA novel time-frequency multilayer network for multivariate time series analysisWeidong Dang0Zhongke Gao1Dongmei Lv2Mingxu Liu3Qing Cai4Xiaolin Hong5School of Electrical and Information Engineering, Tianjin University , Tianjin 300072, People’s Republic of ChinaSchool of Electrical and Information Engineering, Tianjin University , Tianjin 300072, People’s Republic of ChinaSchool of Electrical and Information Engineering, Tianjin University , Tianjin 300072, People’s Republic of ChinaSchool of Electrical and Information Engineering, Tianjin University , Tianjin 300072, People’s Republic of ChinaSchool of Electrical and Information Engineering, Tianjin University , Tianjin 300072, People’s Republic of ChinaSchool of Electrical and Information Engineering, Tianjin University , Tianjin 300072, People’s Republic of ChinaUnveiling complex dynamics of natural systems from a multivariate time series represents a research hotspot in a broad variety of areas. We develop a novel multilayer network analysis framework, i.e. multivariate time-frequency multilayer network (MTFM network), to peer into the complex system dynamics. Through mapping the system features into different frequency-based layers and inferring interactions (edges) among different channels (nodes), the MTFM network allows efficiently integrating time, frequency and spatial information hidden in a multivariate time series. We employ two dynamic systems to illustrate the effectiveness of the MTFM network. We first apply the MTFM network to analyze the 48-channel measurements from industrial oil–water flows and reveal the complex dynamics ruling the transition of different flow patterns. The MTFM network is then utilized to analyze 30-channel fatigue driving electroencephalogram signals. The results demonstrate that MTFM network enables to quantitatively characterize brain behavior associated with fatigue driving. Our MTFM network enriches the multivariate time series analysis theory and helps to better understand the complicated dynamical behaviors underlying complex systems.https://doi.org/10.1088/1367-2630/aaf51cmultilayer networkwavelet analysismutual informationmultivariate time series |
| spellingShingle | Weidong Dang Zhongke Gao Dongmei Lv Mingxu Liu Qing Cai Xiaolin Hong A novel time-frequency multilayer network for multivariate time series analysis New Journal of Physics multilayer network wavelet analysis mutual information multivariate time series |
| title | A novel time-frequency multilayer network for multivariate time series analysis |
| title_full | A novel time-frequency multilayer network for multivariate time series analysis |
| title_fullStr | A novel time-frequency multilayer network for multivariate time series analysis |
| title_full_unstemmed | A novel time-frequency multilayer network for multivariate time series analysis |
| title_short | A novel time-frequency multilayer network for multivariate time series analysis |
| title_sort | novel time frequency multilayer network for multivariate time series analysis |
| topic | multilayer network wavelet analysis mutual information multivariate time series |
| url | https://doi.org/10.1088/1367-2630/aaf51c |
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