Singular Spectrum Analysis for Time Series

  title={Singular Spectrum Analysis for Time Series},
  author={Anatoly A. Zhigljavsky},
  booktitle={International Encyclopedia of Statistical Science},
Singular spectrum analysis (SSA) is a technique of time series analysis and forecasting. It combines elements of classical time series analysis, multivariate statistics, multivariate geometry, dynamical systems and signal processing. SSA aims at decomposing the original series into a sum of a small number of interpretable components such as a slowly varying trend, oscillatory components and a ‘structureless’ noise. It is based on the singular-value decomposition of a specific matrix constructed… CONTINUE READING
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Statistics and Its Interface, Special issue on the singularspectrun analysis for time

  • A. Zhigljavsky
  • 2010
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3 Excerpts

A comprehensive causality test based on the singular spectrum analysis, Causality in Science

  • H. Hassani, A. Zhigljavsky, K. Patterson, A. Soofi
  • 2010
1 Excerpt

The Caterpillar-SSA method for analysis of time series with missing values

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1 Excerpt

An algorithm based on singular spectrum analysis for change-point detection

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1 Excerpt

Monte Carlo SSA: Detecting irregular oscillations in the presence of colored noise

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  • Journal of Climate,
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1 Excerpt

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