STNR: A suffix tree based noise resilient algorithm for periodicity detection in time series databases
Abstract Periodicity detection has been used extensively in predicting the behavior and trends of time series databases. In this paper, we present a noise resilient algorithm for periodicity detection using suffix trees as an underlying data structure. The algorithm not only calculates symbol and se...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Rasheed, Faraz [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2008 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer Science+Business Media, LLC 2008 |
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| Übergeordnetes Werk: |
Enthalten in: Applied intelligence - Springer US, 1991, 32(2008), 3 vom: 04. Sept., Seite 267-278 |
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| Übergeordnetes Werk: |
volume:32 ; year:2008 ; number:3 ; day:04 ; month:09 ; pages:267-278 |
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| DOI / URN: |
10.1007/s10489-008-0144-9 |
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OLC2066095621 |
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| 520 | |a Abstract Periodicity detection has been used extensively in predicting the behavior and trends of time series databases. In this paper, we present a noise resilient algorithm for periodicity detection using suffix trees as an underlying data structure. The algorithm not only calculates symbol and segment periodicity, but also detects the partial (or sequence) periodicity in time series. Most of the existing algorithms fail to perform efficiently in presence of noise; although noise is an inevitable constituent of real world data. The conducted experiments demonstrate that our algorithm performs more efficiently compared to other algorithms in presence of replacement, insertion, deletion or a mixture of any of these types of noise. | ||
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10.1007/s10489-008-0144-9 doi (DE-627)OLC2066095621 (DE-He213)s10489-008-0144-9-p DE-627 ger DE-627 rakwb eng 004 VZ Rasheed, Faraz verfasserin aut STNR: A suffix tree based noise resilient algorithm for periodicity detection in time series databases 2008 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2008 Abstract Periodicity detection has been used extensively in predicting the behavior and trends of time series databases. In this paper, we present a noise resilient algorithm for periodicity detection using suffix trees as an underlying data structure. The algorithm not only calculates symbol and segment periodicity, but also detects the partial (or sequence) periodicity in time series. Most of the existing algorithms fail to perform efficiently in presence of noise; although noise is an inevitable constituent of real world data. The conducted experiments demonstrate that our algorithm performs more efficiently compared to other algorithms in presence of replacement, insertion, deletion or a mixture of any of these types of noise. Time series Periodicity detection Suffix tree Segment periodicity Sequence periodicity Noise resilient Alhajj, Reda aut Enthalten in Applied intelligence Springer US, 1991 32(2008), 3 vom: 04. Sept., Seite 267-278 (DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 0924-669X nnns volume:32 year:2008 number:3 day:04 month:09 pages:267-278 https://doi.org/10.1007/s10489-008-0144-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_130 GBV_ILN_2005 GBV_ILN_2020 AR 32 2008 3 04 09 267-278 |
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10.1007/s10489-008-0144-9 doi (DE-627)OLC2066095621 (DE-He213)s10489-008-0144-9-p DE-627 ger DE-627 rakwb eng 004 VZ Rasheed, Faraz verfasserin aut STNR: A suffix tree based noise resilient algorithm for periodicity detection in time series databases 2008 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2008 Abstract Periodicity detection has been used extensively in predicting the behavior and trends of time series databases. In this paper, we present a noise resilient algorithm for periodicity detection using suffix trees as an underlying data structure. The algorithm not only calculates symbol and segment periodicity, but also detects the partial (or sequence) periodicity in time series. Most of the existing algorithms fail to perform efficiently in presence of noise; although noise is an inevitable constituent of real world data. The conducted experiments demonstrate that our algorithm performs more efficiently compared to other algorithms in presence of replacement, insertion, deletion or a mixture of any of these types of noise. Time series Periodicity detection Suffix tree Segment periodicity Sequence periodicity Noise resilient Alhajj, Reda aut Enthalten in Applied intelligence Springer US, 1991 32(2008), 3 vom: 04. Sept., Seite 267-278 (DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 0924-669X nnns volume:32 year:2008 number:3 day:04 month:09 pages:267-278 https://doi.org/10.1007/s10489-008-0144-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_130 GBV_ILN_2005 GBV_ILN_2020 AR 32 2008 3 04 09 267-278 |
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10.1007/s10489-008-0144-9 doi (DE-627)OLC2066095621 (DE-He213)s10489-008-0144-9-p DE-627 ger DE-627 rakwb eng 004 VZ Rasheed, Faraz verfasserin aut STNR: A suffix tree based noise resilient algorithm for periodicity detection in time series databases 2008 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2008 Abstract Periodicity detection has been used extensively in predicting the behavior and trends of time series databases. In this paper, we present a noise resilient algorithm for periodicity detection using suffix trees as an underlying data structure. The algorithm not only calculates symbol and segment periodicity, but also detects the partial (or sequence) periodicity in time series. Most of the existing algorithms fail to perform efficiently in presence of noise; although noise is an inevitable constituent of real world data. The conducted experiments demonstrate that our algorithm performs more efficiently compared to other algorithms in presence of replacement, insertion, deletion or a mixture of any of these types of noise. Time series Periodicity detection Suffix tree Segment periodicity Sequence periodicity Noise resilient Alhajj, Reda aut Enthalten in Applied intelligence Springer US, 1991 32(2008), 3 vom: 04. Sept., Seite 267-278 (DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 0924-669X nnns volume:32 year:2008 number:3 day:04 month:09 pages:267-278 https://doi.org/10.1007/s10489-008-0144-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_130 GBV_ILN_2005 GBV_ILN_2020 AR 32 2008 3 04 09 267-278 |
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10.1007/s10489-008-0144-9 doi (DE-627)OLC2066095621 (DE-He213)s10489-008-0144-9-p DE-627 ger DE-627 rakwb eng 004 VZ Rasheed, Faraz verfasserin aut STNR: A suffix tree based noise resilient algorithm for periodicity detection in time series databases 2008 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2008 Abstract Periodicity detection has been used extensively in predicting the behavior and trends of time series databases. In this paper, we present a noise resilient algorithm for periodicity detection using suffix trees as an underlying data structure. The algorithm not only calculates symbol and segment periodicity, but also detects the partial (or sequence) periodicity in time series. Most of the existing algorithms fail to perform efficiently in presence of noise; although noise is an inevitable constituent of real world data. The conducted experiments demonstrate that our algorithm performs more efficiently compared to other algorithms in presence of replacement, insertion, deletion or a mixture of any of these types of noise. Time series Periodicity detection Suffix tree Segment periodicity Sequence periodicity Noise resilient Alhajj, Reda aut Enthalten in Applied intelligence Springer US, 1991 32(2008), 3 vom: 04. Sept., Seite 267-278 (DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 0924-669X nnns volume:32 year:2008 number:3 day:04 month:09 pages:267-278 https://doi.org/10.1007/s10489-008-0144-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_130 GBV_ILN_2005 GBV_ILN_2020 AR 32 2008 3 04 09 267-278 |
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Abstract Periodicity detection has been used extensively in predicting the behavior and trends of time series databases. In this paper, we present a noise resilient algorithm for periodicity detection using suffix trees as an underlying data structure. The algorithm not only calculates symbol and segment periodicity, but also detects the partial (or sequence) periodicity in time series. Most of the existing algorithms fail to perform efficiently in presence of noise; although noise is an inevitable constituent of real world data. The conducted experiments demonstrate that our algorithm performs more efficiently compared to other algorithms in presence of replacement, insertion, deletion or a mixture of any of these types of noise. © Springer Science+Business Media, LLC 2008 |
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Abstract Periodicity detection has been used extensively in predicting the behavior and trends of time series databases. In this paper, we present a noise resilient algorithm for periodicity detection using suffix trees as an underlying data structure. The algorithm not only calculates symbol and segment periodicity, but also detects the partial (or sequence) periodicity in time series. Most of the existing algorithms fail to perform efficiently in presence of noise; although noise is an inevitable constituent of real world data. The conducted experiments demonstrate that our algorithm performs more efficiently compared to other algorithms in presence of replacement, insertion, deletion or a mixture of any of these types of noise. © Springer Science+Business Media, LLC 2008 |
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Abstract Periodicity detection has been used extensively in predicting the behavior and trends of time series databases. In this paper, we present a noise resilient algorithm for periodicity detection using suffix trees as an underlying data structure. The algorithm not only calculates symbol and segment periodicity, but also detects the partial (or sequence) periodicity in time series. Most of the existing algorithms fail to perform efficiently in presence of noise; although noise is an inevitable constituent of real world data. The conducted experiments demonstrate that our algorithm performs more efficiently compared to other algorithms in presence of replacement, insertion, deletion or a mixture of any of these types of noise. © Springer Science+Business Media, LLC 2008 |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC2066095621</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230502204857.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200820s2008 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10489-008-0144-9</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2066095621</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s10489-008-0144-9-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">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Rasheed, Faraz</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">STNR: A suffix tree based noise resilient algorithm for periodicity detection in time series databases</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2008</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 2008</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Periodicity detection has been used extensively in predicting the behavior and trends of time series databases. 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