Automatic Signal Extraction for Stationary and Non-Stationary Time Series by Circulant SSA

Juan Bógalo, Pilar Poncela and Eva Senra

MPRA Paper from University Library of Munich, Germany

Abstract: 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.

Keywords: circulant matrices; signal extraction; singular spectrum analysis; non-parametric; time series; Toeplitz matrices. (search for similar items in EconPapers)
JEL-codes: C22 E32 (search for similar items in EconPapers)
Date: 2017-01-05
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-mac
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:76023

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