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Imaging feature-based clustering of financial time series
Timeseries representation underpin our ability to understand and predict the change of natural system. Series are often predicated on our choice of highly redundant factors, and in fact, the system is driven by a much smaller set of latent intrinsic keys. It means that a better representation of dat...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10370745/ https://www.ncbi.nlm.nih.gov/pubmed/37494391 http://dx.doi.org/10.1371/journal.pone.0288836 |
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author | Wu, Jun Zhang, Zelin Tong, Rui Zhou, Yuan Hu, Zhengfa Liu, Kaituo |
author_facet | Wu, Jun Zhang, Zelin Tong, Rui Zhou, Yuan Hu, Zhengfa Liu, Kaituo |
author_sort | Wu, Jun |
collection | PubMed |
description | Timeseries representation underpin our ability to understand and predict the change of natural system. Series are often predicated on our choice of highly redundant factors, and in fact, the system is driven by a much smaller set of latent intrinsic keys. It means that a better representation of data makes points in phase space clearly for researchers. Specially, a 2D structure of timeseries could combine the trend and correlation characters of different periods in timeseries together, which provides more clear information for top tasks. In this work, the effectiveness of 2D structure of timeseries is investigated in clustering tasks. There are 4 kinds of methods that the Recurrent Plot (RP), the Gramian Angular Summation Field (GASF), the Gramian Angular Differential Field (GADF) and the Markov Transition Field (MTF) have been adopted in the analysis. By classifying the CSI300 and S&P500 indexes, we found that the RP imaging series are valid in recognizing abnormal fluctuations of financial timeseries, as the silhouette values of clusters are over 0.6 to 1. Compared with segment methods, the 2D models have the lowest instability value of 0. It verifies that the SIFT features of RP images take advantage of the volatility of financial series for clustering tasks. |
format | Online Article Text |
id | pubmed-10370745 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-103707452023-07-27 Imaging feature-based clustering of financial time series Wu, Jun Zhang, Zelin Tong, Rui Zhou, Yuan Hu, Zhengfa Liu, Kaituo PLoS One Research Article Timeseries representation underpin our ability to understand and predict the change of natural system. Series are often predicated on our choice of highly redundant factors, and in fact, the system is driven by a much smaller set of latent intrinsic keys. It means that a better representation of data makes points in phase space clearly for researchers. Specially, a 2D structure of timeseries could combine the trend and correlation characters of different periods in timeseries together, which provides more clear information for top tasks. In this work, the effectiveness of 2D structure of timeseries is investigated in clustering tasks. There are 4 kinds of methods that the Recurrent Plot (RP), the Gramian Angular Summation Field (GASF), the Gramian Angular Differential Field (GADF) and the Markov Transition Field (MTF) have been adopted in the analysis. By classifying the CSI300 and S&P500 indexes, we found that the RP imaging series are valid in recognizing abnormal fluctuations of financial timeseries, as the silhouette values of clusters are over 0.6 to 1. Compared with segment methods, the 2D models have the lowest instability value of 0. It verifies that the SIFT features of RP images take advantage of the volatility of financial series for clustering tasks. Public Library of Science 2023-07-26 /pmc/articles/PMC10370745/ /pubmed/37494391 http://dx.doi.org/10.1371/journal.pone.0288836 Text en © 2023 Wu et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Wu, Jun Zhang, Zelin Tong, Rui Zhou, Yuan Hu, Zhengfa Liu, Kaituo Imaging feature-based clustering of financial time series |
title | Imaging feature-based clustering of financial time series |
title_full | Imaging feature-based clustering of financial time series |
title_fullStr | Imaging feature-based clustering of financial time series |
title_full_unstemmed | Imaging feature-based clustering of financial time series |
title_short | Imaging feature-based clustering of financial time series |
title_sort | imaging feature-based clustering of financial time series |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10370745/ https://www.ncbi.nlm.nih.gov/pubmed/37494391 http://dx.doi.org/10.1371/journal.pone.0288836 |
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