Multilayer horizontal visibility graphs for multivariate time series analysis: Multilayer horizontal visibility graphs...: V. Freitas Silva et al.—Editorial.
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| Title: | Multilayer horizontal visibility graphs for multivariate time series analysis: Multilayer horizontal visibility graphs...: V. Freitas Silva et al.—Editorial. |
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| Authors: | Freitas Silva, Vanessa1 (AUTHOR) vanessa.silva@fc.up.pt, Silva, Maria Eduarda2 (AUTHOR) mesilva@fep.up.pt, Ribeiro, Pedro1 (AUTHOR) pribeiro@fc.up.pt, Silva, Fernando1 (AUTHOR) fmsilva@fc.up.pt |
| Source: | Data Mining & Knowledge Discovery. May2025, Vol. 39 Issue 3, p1-42. 42p. |
| Subjects: | Time series analysis, Timestamps, Data analysis |
| Abstract: | Multivariate time series analysis is a vital but challenging task, with multidisciplinary applicability, tackling the characterization of multiple interconnected variables over time and their dependencies. Traditional methodologies often adapt univariate approaches or rely on assumptions specific to certain domains or problems, presenting limitations. A recent promising alternative is to map multivariate time series into high-level network structures such as multiplex networks, with past work relying on connecting successive time series components with interconnections between contemporary timestamps. In this work, we first define a novel cross-horizontal visibility mapping between lagged timestamps of different time series and then introduce the concept of multilayer horizontal visibility graphs. This allows describing cross-dimension dependencies via inter-layer edges, leveraging the entire structure of multilayer networks. To this end, a novel parameter-free topological measure is proposed and common measures are extended for the multilayer setting. Our approach is general and applicable to any kind of multivariate time series data. We provide an extensive experimental evaluation with both synthetic and real-world datasets. We first explore the proposed methodology and the data properties highlighted by each measure, showing that inter-layer edges based on cross-horizontal visibility preserve more information than previous mappings, while also complementing the information captured by commonly used intra-layer edges. We then illustrate the applicability and validity of our approach in multivariate time series mining tasks, showcasing its potential for enhanced data analysis and insights. [ABSTRACT FROM AUTHOR] |
| Copyright of Data Mining & Knowledge Discovery is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 183406067 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Multilayer horizontal visibility graphs for multivariate time series analysis: Multilayer horizontal visibility graphs...: V. Freitas Silva et al.—Editorial. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Freitas+Silva%2C+Vanessa%22">Freitas Silva, Vanessa</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> vanessa.silva@fc.up.pt</i><br /><searchLink fieldCode="AR" term="%22Silva%2C+Maria+Eduarda%22">Silva, Maria Eduarda</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> mesilva@fep.up.pt</i><br /><searchLink fieldCode="AR" term="%22Ribeiro%2C+Pedro%22">Ribeiro, Pedro</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> pribeiro@fc.up.pt</i><br /><searchLink fieldCode="AR" term="%22Silva%2C+Fernando%22">Silva, Fernando</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> fmsilva@fc.up.pt</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Data+Mining+%26+Knowledge+Discovery%22">Data Mining & Knowledge Discovery</searchLink>. May2025, Vol. 39 Issue 3, p1-42. 42p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Time+series+analysis%22">Time series analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Timestamps%22">Timestamps</searchLink><br /><searchLink fieldCode="DE" term="%22Data+analysis%22">Data analysis</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Multivariate time series analysis is a vital but challenging task, with multidisciplinary applicability, tackling the characterization of multiple interconnected variables over time and their dependencies. Traditional methodologies often adapt univariate approaches or rely on assumptions specific to certain domains or problems, presenting limitations. A recent promising alternative is to map multivariate time series into high-level network structures such as multiplex networks, with past work relying on connecting successive time series components with interconnections between contemporary timestamps. In this work, we first define a novel cross-horizontal visibility mapping between lagged timestamps of different time series and then introduce the concept of multilayer horizontal visibility graphs. This allows describing cross-dimension dependencies via inter-layer edges, leveraging the entire structure of multilayer networks. To this end, a novel parameter-free topological measure is proposed and common measures are extended for the multilayer setting. Our approach is general and applicable to any kind of multivariate time series data. We provide an extensive experimental evaluation with both synthetic and real-world datasets. We first explore the proposed methodology and the data properties highlighted by each measure, showing that inter-layer edges based on cross-horizontal visibility preserve more information than previous mappings, while also complementing the information captured by commonly used intra-layer edges. We then illustrate the applicability and validity of our approach in multivariate time series mining tasks, showcasing its potential for enhanced data analysis and insights. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Data Mining & Knowledge Discovery is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s10618-025-01089-4 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 42 StartPage: 1 Subjects: – SubjectFull: Time series analysis Type: general – SubjectFull: Timestamps Type: general – SubjectFull: Data analysis Type: general Titles: – TitleFull: Multilayer horizontal visibility graphs for multivariate time series analysis: Multilayer horizontal visibility graphs...: V. Freitas Silva et al.—Editorial. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Freitas Silva, Vanessa – PersonEntity: Name: NameFull: Silva, Maria Eduarda – PersonEntity: Name: NameFull: Ribeiro, Pedro – PersonEntity: Name: NameFull: Silva, Fernando IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 13845810 Numbering: – Type: volume Value: 39 – Type: issue Value: 3 Titles: – TitleFull: Data Mining & Knowledge Discovery Type: main |
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