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

Bógalo, Juan and Poncela, Pilar and Senra, Eva (2017): Automatic Signal Extraction for Stationary and Non-Stationary Time Series by Circulant SSA.

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.

• Item Type: MPRA Paper
• Language: English
• Keywords: circulant matrices, signal extraction, singular spectrum analysis, non-parametric, time series, Toeplitz matrices.
• Subjects: C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C22 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes; E - Macroeconomics and Monetary Economics > E3 - Prices, Business Fluctuations, and Cycles > E32 - Business Fluctuations ; Cycles

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