title: Improvement of separability of time series in singular spectrum analysis by means of a method of independent component analysis
metadata.name: dc.contributor.author
metadata.value: Golyandina, Nina E.
metadata.lang: -
metadata.name: dc.contributor.author
metadata.value: Lomtev, Maksim A.
metadata.lang: -
metadata.name: dc.date.accessioned
metadata.value: 2016-09-01T14:57:47Z
metadata.lang: -
metadata.name: dc.date.available
metadata.value: 2016-09-01T14:57:47Z
metadata.lang: -
metadata.name: dc.date.issued
metadata.value: 2016-03
metadata.lang: -
metadata.name: dc.identifier.uri
metadata.value: http://hdl.handle.net/11701/3833
metadata.lang: -
metadata.name: dc.description.abstract
metadata.value: Separation of signal components is an important problem of time series analysis. For example, the solution
  of this problem allows one to extract a trend and to separate harmonic signals with different frequencies. In
  the paper, a modification of the singular spectrum analysis (SSA) methods is considered for improvement
  of separability of time series components. The new method is named SSA-AMUSE, since it is based on
  the method AMUSE used for application of independent component analysis to signal separation. The
  suggested modification weakens the conditions of the so-called strong separability and thereby improves
  the quality of separation of time series components with comparison with similar methods. The paper
  contains the proof of the algorithm and also the conditions of separability for the considered modification.
  Besides the exact separability, the asymptotic separability is also considered. The separability conditions
  are applied to the case of two harmonic time series. It appears that the separability by SSA-AMUSE
  does not depend on amplitudes of the separated harmonics, while the Basic SSA method needs different
  amplitudes. A numerical example demonstrates an advantage of the SSA-AMUSE method in comparison
  with a similar modification. Refs 9. Figs 1.
metadata.lang: en_GB
metadata.name: dc.description.sponsorship
metadata.value: Работа выполнена при финансовой поддержке РФФИ (грант №15-04-06480).
metadata.lang: en_GB
metadata.name: dc.language.iso
metadata.value: ru
metadata.lang: en_GB
metadata.name: dc.publisher
metadata.value: St Petersburg State University
metadata.lang: en_GB
metadata.name: dc.relation.ispartofseries
metadata.value: Vestnik of St Petersburg University. Series 1. Mathematics. Mechanics. Astronomy;Vol. 3 (61); Issue 1
metadata.lang: -
metadata.name: dc.subject
metadata.value: singular spectrum analysis
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: independent component analysis
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: time series
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: separability
metadata.lang: en_GB
metadata.name: dc.title
metadata.value: Improvement of separability of time series in singular spectrum analysis by means of a method of independent component analysis
metadata.lang: en_GB
metadata.name: dc.type
metadata.value: Article
metadata.lang: en_GB
resources.filename: Golyandina_Lomtev.pdf
resources.url: https://dspace.spbu.ru/bitstream/11701/3833/1/Golyandina_Lomtev.pdf
resources.format: Adobe PDF
resources.size: 313,46 kB