Eigen-entropy based time series signatures to support multivariate time series classification.
Correlation coefficient
Dense multi scale entropy
Eigen-entropy
Eigenvalue
Multivariate time series classification
Time series signatures
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
12 Jul 2024
12 Jul 2024
Historique:
received:
24
03
2024
accepted:
05
07
2024
medline:
12
7
2024
pubmed:
12
7
2024
entrez:
11
7
2024
Statut:
epublish
Résumé
Most current algorithms for multivariate time series classification tend to overlook the correlations between time series of different variables. In this research, we propose a framework that leverages Eigen-entropy along with a cumulative moving window to derive time series signatures to support the classification task. These signatures are enumerations of correlations among different time series considering the temporal nature of the dataset. To manage dataset's dynamic nature, we employ preprocessing with dense multi scale entropy. Consequently, the proposed framework, Eigen-entropy-based Time Series Signatures, captures correlations among multivariate time series without losing its temporal and dynamic aspects. The efficacy of our algorithm is assessed using six binary datasets sourced from the University of East Anglia, in addition to a publicly available gait dataset and an institutional sepsis dataset from the Mayo Clinic. We use recall as the evaluation metric to compare our approach against baseline algorithms, including dependent dynamic time warping with 1 nearest neighbor and multivariate multi-scale permutation entropy. Our method demonstrates superior performance in terms of recall for seven out of the eight datasets.
Identifiants
pubmed: 38992044
doi: 10.1038/s41598-024-66953-7
pii: 10.1038/s41598-024-66953-7
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
16076Subventions
Organisme : National Science Foundation (NSF) - Partnership for Innovation - "Avoiding Kidney Injuries"
ID : 2122901
Informations de copyright
© 2024. The Author(s).
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