A novel way to detect correlations on multi-time scales, with temporal evolution and for multi-variables
In this paper, two new methods, Temporal evolution of Detrended Cross-Correlation Analysis (TDCCA) and Temporal evolution of Detrended Partial-Cross-Correlation Analysis (TDPCCA), are proposed by generalizing DCCA and DPCCA. Applying TDCCA/TDPCCA, it is possible to study correlations on multi-time s...
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ftpubmed:oai:pubmedcentral.nih.gov:4904221 2023-05-15T17:33:53+02:00 A novel way to detect correlations on multi-time scales, with temporal evolution and for multi-variables Yuan, Naiming Xoplaki, Elena Zhu, Congwen Luterbacher, Juerg 2016-06-13 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4904221/ http://www.ncbi.nlm.nih.gov/pubmed/27293028 https://doi.org/10.1038/srep27707 en eng Nature Publishing Group http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4904221/ http://www.ncbi.nlm.nih.gov/pubmed/27293028 http://dx.doi.org/10.1038/srep27707 Copyright © 2016, Macmillan Publishers Limited http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ CC-BY Article Text 2016 ftpubmed https://doi.org/10.1038/srep27707 2016-06-19T00:16:17Z In this paper, two new methods, Temporal evolution of Detrended Cross-Correlation Analysis (TDCCA) and Temporal evolution of Detrended Partial-Cross-Correlation Analysis (TDPCCA), are proposed by generalizing DCCA and DPCCA. Applying TDCCA/TDPCCA, it is possible to study correlations on multi-time scales and over different periods. To illustrate their properties, we used two climatological examples: i) Global Sea Level (GSL) versus North Atlantic Oscillation (NAO); and ii) Summer Rainfall over Yangtze River (SRYR) versus previous winter Pacific Decadal Oscillation (PDO). We find significant correlations between GSL and NAO on time scales of 60 to 140 years, but the correlations are non-significant between 1865–1875. As for SRYR and PDO, significant correlations are found on time scales of 30 to 35 years, but the correlations are more pronounced during the recent 30 years. By combining TDCCA/TDPCCA and DCCA/DPCCA, we proposed a new correlation-detection system, which compared to traditional methods, can objectively show how two time series are related (on which time scale, during which time period). These are important not only for diagnosis of complex system, but also for better designs of prediction models. Therefore, the new methods offer new opportunities for applications in natural sciences, such as ecology, economy, sociology and other research fields. Text North Atlantic North Atlantic oscillation PubMed Central (PMC) Pacific Scientific Reports 6 1 |
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Article Yuan, Naiming Xoplaki, Elena Zhu, Congwen Luterbacher, Juerg A novel way to detect correlations on multi-time scales, with temporal evolution and for multi-variables |
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Article |
description |
In this paper, two new methods, Temporal evolution of Detrended Cross-Correlation Analysis (TDCCA) and Temporal evolution of Detrended Partial-Cross-Correlation Analysis (TDPCCA), are proposed by generalizing DCCA and DPCCA. Applying TDCCA/TDPCCA, it is possible to study correlations on multi-time scales and over different periods. To illustrate their properties, we used two climatological examples: i) Global Sea Level (GSL) versus North Atlantic Oscillation (NAO); and ii) Summer Rainfall over Yangtze River (SRYR) versus previous winter Pacific Decadal Oscillation (PDO). We find significant correlations between GSL and NAO on time scales of 60 to 140 years, but the correlations are non-significant between 1865–1875. As for SRYR and PDO, significant correlations are found on time scales of 30 to 35 years, but the correlations are more pronounced during the recent 30 years. By combining TDCCA/TDPCCA and DCCA/DPCCA, we proposed a new correlation-detection system, which compared to traditional methods, can objectively show how two time series are related (on which time scale, during which time period). These are important not only for diagnosis of complex system, but also for better designs of prediction models. Therefore, the new methods offer new opportunities for applications in natural sciences, such as ecology, economy, sociology and other research fields. |
format |
Text |
author |
Yuan, Naiming Xoplaki, Elena Zhu, Congwen Luterbacher, Juerg |
author_facet |
Yuan, Naiming Xoplaki, Elena Zhu, Congwen Luterbacher, Juerg |
author_sort |
Yuan, Naiming |
title |
A novel way to detect correlations on multi-time scales, with temporal evolution and for multi-variables |
title_short |
A novel way to detect correlations on multi-time scales, with temporal evolution and for multi-variables |
title_full |
A novel way to detect correlations on multi-time scales, with temporal evolution and for multi-variables |
title_fullStr |
A novel way to detect correlations on multi-time scales, with temporal evolution and for multi-variables |
title_full_unstemmed |
A novel way to detect correlations on multi-time scales, with temporal evolution and for multi-variables |
title_sort |
novel way to detect correlations on multi-time scales, with temporal evolution and for multi-variables |
publisher |
Nature Publishing Group |
publishDate |
2016 |
url |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4904221/ http://www.ncbi.nlm.nih.gov/pubmed/27293028 https://doi.org/10.1038/srep27707 |
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Pacific |
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Pacific |
genre |
North Atlantic North Atlantic oscillation |
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North Atlantic North Atlantic oscillation |
op_relation |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4904221/ http://www.ncbi.nlm.nih.gov/pubmed/27293028 http://dx.doi.org/10.1038/srep27707 |
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Copyright © 2016, Macmillan Publishers Limited http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
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CC-BY |
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https://doi.org/10.1038/srep27707 |
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Scientific Reports |
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