Estimation and classification of temporal trends to support integrated ecosystem assessment
We propose a trend estimation and classification (TREC) approach to estimating dominant common trends among multivariate time series observations. Our methods are based on two statistical procedures that includes trend modelling and discriminant analysis for classifying similar trend (common trend)...
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Online Access: | https://hdl.handle.net/11250/2728250 https://doi.org/10.1093/icesjms/fsaa111 |
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ftimr:oai:imr.brage.unit.no:11250/2728250 2023-05-15T15:38:56+02:00 Estimation and classification of temporal trends to support integrated ecosystem assessment Solvang, Hiroko Kato Planque, Benjamin 2020 application/pdf https://hdl.handle.net/11250/2728250 https://doi.org/10.1093/icesjms/fsaa111 eng eng Havforskningsinstituttet: 14565 Norges forskningsråd: 288192 ICES Journal of Marine Science. 2020, 77 (7-8), 2529-2540. urn:issn:1054-3139 https://hdl.handle.net/11250/2728250 https://doi.org/10.1093/icesjms/fsaa111 cristin:1888993 2529-2540 77 ICES Journal of Marine Science 7-8 Peer reviewed Journal article 2020 ftimr https://doi.org/10.1093/icesjms/fsaa111 2021-09-23T20:15:58Z We propose a trend estimation and classification (TREC) approach to estimating dominant common trends among multivariate time series observations. Our methods are based on two statistical procedures that includes trend modelling and discriminant analysis for classifying similar trend (common trend) classes. We use simulations to evaluate the proposed approach and compare it with a relevant dynamic factor analysis in the time domain, which was recently proposed to estimate common trends in fisheries time series. We apply the TREC approach to the multivariate short time series datasets investigated by the ICES integrated assessment working groups for the Norwegian Sea and the Barents Sea. The proposed approach is robust for application to short time series, and it directly identifies and classifies the dominant trends underlying observations. Based on the classified trend classes, we suggest that communication among stakeholders like marine managers, industry representatives, non-governmental organizations, and governmental agencies can be enhanced by finding the common tendency between a biological community in a marine ecosystem and the environmental factors, as well as by the icons produced by generalizing common trend patterns. publishedVersion Article in Journal/Newspaper Barents Sea Norwegian Sea Institute for Marine Research: Brage IMR Barents Sea Norwegian Sea ICES Journal of Marine Science 77 7-8 2529 2540 |
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Open Polar |
collection |
Institute for Marine Research: Brage IMR |
op_collection_id |
ftimr |
language |
English |
description |
We propose a trend estimation and classification (TREC) approach to estimating dominant common trends among multivariate time series observations. Our methods are based on two statistical procedures that includes trend modelling and discriminant analysis for classifying similar trend (common trend) classes. We use simulations to evaluate the proposed approach and compare it with a relevant dynamic factor analysis in the time domain, which was recently proposed to estimate common trends in fisheries time series. We apply the TREC approach to the multivariate short time series datasets investigated by the ICES integrated assessment working groups for the Norwegian Sea and the Barents Sea. The proposed approach is robust for application to short time series, and it directly identifies and classifies the dominant trends underlying observations. Based on the classified trend classes, we suggest that communication among stakeholders like marine managers, industry representatives, non-governmental organizations, and governmental agencies can be enhanced by finding the common tendency between a biological community in a marine ecosystem and the environmental factors, as well as by the icons produced by generalizing common trend patterns. publishedVersion |
format |
Article in Journal/Newspaper |
author |
Solvang, Hiroko Kato Planque, Benjamin |
spellingShingle |
Solvang, Hiroko Kato Planque, Benjamin Estimation and classification of temporal trends to support integrated ecosystem assessment |
author_facet |
Solvang, Hiroko Kato Planque, Benjamin |
author_sort |
Solvang, Hiroko Kato |
title |
Estimation and classification of temporal trends to support integrated ecosystem assessment |
title_short |
Estimation and classification of temporal trends to support integrated ecosystem assessment |
title_full |
Estimation and classification of temporal trends to support integrated ecosystem assessment |
title_fullStr |
Estimation and classification of temporal trends to support integrated ecosystem assessment |
title_full_unstemmed |
Estimation and classification of temporal trends to support integrated ecosystem assessment |
title_sort |
estimation and classification of temporal trends to support integrated ecosystem assessment |
publishDate |
2020 |
url |
https://hdl.handle.net/11250/2728250 https://doi.org/10.1093/icesjms/fsaa111 |
geographic |
Barents Sea Norwegian Sea |
geographic_facet |
Barents Sea Norwegian Sea |
genre |
Barents Sea Norwegian Sea |
genre_facet |
Barents Sea Norwegian Sea |
op_source |
2529-2540 77 ICES Journal of Marine Science 7-8 |
op_relation |
Havforskningsinstituttet: 14565 Norges forskningsråd: 288192 ICES Journal of Marine Science. 2020, 77 (7-8), 2529-2540. urn:issn:1054-3139 https://hdl.handle.net/11250/2728250 https://doi.org/10.1093/icesjms/fsaa111 cristin:1888993 |
op_doi |
https://doi.org/10.1093/icesjms/fsaa111 |
container_title |
ICES Journal of Marine Science |
container_volume |
77 |
container_issue |
7-8 |
container_start_page |
2529 |
op_container_end_page |
2540 |
_version_ |
1766370352529670144 |