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A study of singular spectrum analysis with global optimization techniques

Author

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  • Moody Chu
  • Matthew Lin
  • Liqi Wang
Abstract
Singular spectrum analysis has recently become an attractive tool in a broad range of applications. Its main mechanism of alternating between rank reduction and Hankel projection to produce an approximation to a particular component of the original time series, however, deserves further mathematical justification. One paramount question to ask is how good an approximation that such a straightforward apparatus can provide when comparing to the absolute optimal solution. This paper reexamines this issue by exploiting a natural parametrization of a general Hankel matrix via its Vandermonde factorization. Such a formulation makes it possible to recast the notion of singular spectrum analysis as a semi-linear least squares problem over a compact feasible set, whence global optimization techniques can be employed to find the absolute best approximation. This framework might not be immediately suitable for practical application because global optimization is expectedly more expensive, but it does provide a theoretical baseline for comparison. As such, our empirical results indicate that the simpler SSA algorithm usually is amazingly sufficient as a handy tool for constructing exploratory model. The more complicated global methods could be used as an alternative of rigorous affirmative procedure for verifying or assessing the quality of approximation. Copyright Springer Science+Business Media New York 2014

Suggested Citation

  • Moody Chu & Matthew Lin & Liqi Wang, 2014. "A study of singular spectrum analysis with global optimization techniques," Journal of Global Optimization, Springer, vol. 60(3), pages 551-574, November.
    • Handle: RePEc:spr:jglopt:v:60:y:2014:i:3:p:551-574
    DOI: 10.1007/s10898-013-0117-3
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    References listed on IDEAS

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    1. Kerry Patterson & Hossein Hassani & Saeed Heravi & Anatoly Zhigljavsky, 2011. "Multivariate singular spectrum analysis for forecasting revisions to real-time data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(10), pages 2183-2211.
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    3. Hassani, Hossein & Heravi, Saeed & Zhigljavsky, Anatoly, 2009. "Forecasting European industrial production with singular spectrum analysis," International Journal of Forecasting, Elsevier, vol. 25(1), pages 103-118.
    4. Hassani, Hossein, 2007. "Singular Spectrum Analysis: Methodology and Comparison," MPRA Paper 4991, University Library of Munich, Germany.
    5. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    6. Zsolt Ugray & Leon Lasdon & John Plummer & Fred Glover & James Kelly & Rafael Martí, 2007. "Scatter Search and Local NLP Solvers: A Multistart Framework for Global Optimization," INFORMS Journal on Computing, INFORMS, vol. 19(3), pages 328-340, August.
    7. Anatoly Zhigljavsky & Antanas Žilinskas, 2008. "Stochastic Global Optimization," Springer Optimization and Its Applications, Springer, number 978-0-387-74740-8, June.
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