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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...

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Detalles Bibliográficos
Autores principales: Wu, Jun, Zhang, Zelin, Tong, Rui, Zhou, Yuan, Hu, Zhengfa, Liu, Kaituo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
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.
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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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