Automatic signal extraction for stationary and non-stationary time series by circulant SSA
Fecha de publicación
2017-01-08Patrocinadores
Ministerio de Economía y Competitividad
Cita bibliográfica
MPRA Paper No. 76023, 2017
Palabras clave
Circulant matrices
Signal extraction
Singular spectrum analysis
Nonparametric
Time series
Toeplitz matrices
Proyectos
info:eu-repo/grantAgreement/MINECO//ECO2015-70331-C2-1-R/ES/Análisis de actividad económica mediante indicadores y evaluación de políticas públicas
info:eu-repo/grantAgreement/MINECO//ECO2015-66593-P/ES/"BIG DATA" Y DATOS COMPLEJOS EN EMPRESA Y FINANZAS
info:eu-repo/grantAgreement/MINECO//ECO2014-56676C2-2-P/ES
Tipo de documento
info:eu-repo/semantics/workingPaper
Versión del editor
https://mpra.ub.uni-muenchen.de/id/eprint/76023Derechos
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
Derechos de acceso
info:eu-repo/semantics/openAccess
Resumen
Singular Spectrum Analysis (SSA) is a nonparametric tecnique for signal extraction in time series based on principal components. However, it requires the intervention of the analyst to identify the frequencies associated to the extracted principal components. We propose a new variant of SSA, Circulant SSA (CSSA) that automatically makes this association. We also prove the validity of CSSA for the nonstationary case. Through several sets of simulations, we show the good properties of our approach: it is reliable, fast, automatic and produces strongly separable elementary components by frequency. Finally, we apply Circulant SSA to the Industrial Production Index of six countries. We use it to deseasonalize the series and to illustrate that it also reproduces a cycle in accordance to the dated recessions from the OECD
Ficheros en el ítem
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automatic_senra_MPRA_2017.pdf | 562.1Kb |
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