Time Series, Spectral Densities and Robust Functional Clustering
Abstract In this work, a robust clustering algorithm for stationary time series is proposed. The algorithm is based on the use of estimated spectral densities, which are considered as functional data, as the basic characteristic of stationary time series for clustering purposes. A robust algorithm f...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Rivera-García, D. [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2018 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer Science+Business Media, LLC, part of Springer Nature 2018 |
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| Übergeordnetes Werk: |
Enthalten in: Neural processing letters - Springer US, 1994, 52(2018), 1 vom: 01. Okt., Seite 135-152 |
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| Übergeordnetes Werk: |
volume:52 ; year:2018 ; number:1 ; day:01 ; month:10 ; pages:135-152 |
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| DOI / URN: |
10.1007/s11063-018-9926-1 |
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10.1007/s11063-018-9926-1 doi (DE-627)OLC2118889011 (DE-He213)s11063-018-9926-1-p DE-627 ger DE-627 rakwb eng 000 VZ Rivera-García, D. verfasserin (orcid)0000-0002-3484-8195 aut Time Series, Spectral Densities and Robust Functional Clustering 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract In this work, a robust clustering algorithm for stationary time series is proposed. The algorithm is based on the use of estimated spectral densities, which are considered as functional data, as the basic characteristic of stationary time series for clustering purposes. A robust algorithm for functional data is then applied to the set of spectral densities. Trimming techniques and restrictions on the scatter within groups reduce the effect of noise in the data and help to prevent the identification of spurious clusters. The procedure is tested in a simulation study and is also applied to a real data set. Time series clustering Robust clustering Robust functional data clustering Spectral analysis García-Escudero, L. A. (orcid)0000-0002-7617-3034 aut Mayo-Iscar, A. (orcid)0000-0003-0951-6508 aut Ortega, J. (orcid)0000-0001-9806-1693 aut Enthalten in Neural processing letters Springer US, 1994 52(2018), 1 vom: 01. Okt., Seite 135-152 (DE-627)198692617 (DE-600)1316823-X (DE-576)052842762 1370-4621 nnns volume:52 year:2018 number:1 day:01 month:10 pages:135-152 https://doi.org/10.1007/s11063-018-9926-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PSY SSG-OLC-MAT GBV_ILN_70 AR 52 2018 1 01 10 135-152 |
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10.1007/s11063-018-9926-1 doi (DE-627)OLC2118889011 (DE-He213)s11063-018-9926-1-p DE-627 ger DE-627 rakwb eng 000 VZ Rivera-García, D. verfasserin (orcid)0000-0002-3484-8195 aut Time Series, Spectral Densities and Robust Functional Clustering 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract In this work, a robust clustering algorithm for stationary time series is proposed. The algorithm is based on the use of estimated spectral densities, which are considered as functional data, as the basic characteristic of stationary time series for clustering purposes. A robust algorithm for functional data is then applied to the set of spectral densities. Trimming techniques and restrictions on the scatter within groups reduce the effect of noise in the data and help to prevent the identification of spurious clusters. The procedure is tested in a simulation study and is also applied to a real data set. Time series clustering Robust clustering Robust functional data clustering Spectral analysis García-Escudero, L. A. (orcid)0000-0002-7617-3034 aut Mayo-Iscar, A. (orcid)0000-0003-0951-6508 aut Ortega, J. (orcid)0000-0001-9806-1693 aut Enthalten in Neural processing letters Springer US, 1994 52(2018), 1 vom: 01. Okt., Seite 135-152 (DE-627)198692617 (DE-600)1316823-X (DE-576)052842762 1370-4621 nnns volume:52 year:2018 number:1 day:01 month:10 pages:135-152 https://doi.org/10.1007/s11063-018-9926-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PSY SSG-OLC-MAT GBV_ILN_70 AR 52 2018 1 01 10 135-152 |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">OLC2118889011</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230504162835.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">230504s2018 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11063-018-9926-1</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2118889011</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s11063-018-9926-1-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">000</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Rivera-García, D.</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0002-3484-8195</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Time Series, Spectral Densities and Robust Functional Clustering</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2018</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer Science+Business Media, LLC, part of Springer Nature 2018</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract In this work, a robust clustering algorithm for stationary time series is proposed. The algorithm is based on the use of estimated spectral densities, which are considered as functional data, as the basic characteristic of stationary time series for clustering purposes. A robust algorithm for functional data is then applied to the set of spectral densities. Trimming techniques and restrictions on the scatter within groups reduce the effect of noise in the data and help to prevent the identification of spurious clusters. The procedure is tested in a simulation study and is also applied to a real data set.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Time series clustering</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Robust clustering</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Robust functional data clustering</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Spectral analysis</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">García-Escudero, L. A.</subfield><subfield code="0">(orcid)0000-0002-7617-3034</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Mayo-Iscar, A.</subfield><subfield code="0">(orcid)0000-0003-0951-6508</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ortega, J.</subfield><subfield code="0">(orcid)0000-0001-9806-1693</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Neural processing letters</subfield><subfield code="d">Springer US, 1994</subfield><subfield code="g">52(2018), 1 vom: 01. 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