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Singular Spectrum Analysis: Methodology and Comparison

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  • Hassani, Hossein

Abstract

In recent years Singular Spectrum Analysis (SSA), used as a powerful technique in time series analysis, has been developed and applied to many practical problems. In this paper, the performance of the SSA technique has been considered by applying it to a well-known time series data set, namely, monthly accidental deaths in the USA. The results are compared with those obtained using Box-Jenkins SARIMA models, the ARAR algorithm and the Holt-Winter algorithm (as described in Brockwell and Davis (2002)). The results show that the SSA technique gives a much more accurate forecast than the other methods indicated above.

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Bibliographic Info

Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 4991.

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Date of creation: 01 Apr 2007
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Publication status: Published in Journal of Data Science 2.5(2007): pp. 239-257
Handle: RePEc:pra:mprapa:4991

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Keywords: ARAR algorithm; Box-Jenkins SARIMA models; Holt-Winter algorithm; singular spectrum analysis (SSA); USA monthly accidental deaths series;

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Cited by:
  1. 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.
  2. repec:rdg:wpaper:em-dp2013-04 is not listed on IDEAS
  3. Christina Beneki & Bruno Eeckels & Costas Leon, 2012. "Signal Extraction and Forecasting of the UK Tourism Income Time Series: A Singular Spectrum Analysis Approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 31(5), pages 391-400, 08.
  4. Dimitrios Thomakos & Hossein Hassani & Kerry Patterson, 2013. "Optimal Linear Filtering, Smoothing and Trend Extraction for the m-th Differences of a Unit Root Process: A Singular Spectrum Analysis Approach," Economics & Management Discussion Papers em-dp2013-04, Henley Business School, Reading University.
  5. M. Atikur Rahman Khan & D.S. Poskitt, 2014. "On The Theory and Practice of Singular Spectrum Analysis Forecasting," Monash Econometrics and Business Statistics Working Papers 3/14, Monash University, Department of Econometrics and Business Statistics.
  6. Menezes, Rui & Dionísio, Andreia & Hassani, Hossein, 2012. "On the globalization of stock markets: An application of Vector Error Correction Model, Mutual Information and Singular Spectrum Analysis to the G7 countries," The Quarterly Review of Economics and Finance, Elsevier, vol. 52(4), pages 369-384.
  7. Telesca, Luciano & Lovallo, Michele & Babayev, Gulam & Kadirov, Fakhraddin, 2013. "Spectral and informational analysis of seismicity: An application to the 1996–2012 seismicity of the Northern Caucasus–Azerbaijan part of the greater Caucasus–Kopet Dag region," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(23), pages 6064-6078.
  8. Md Atikur Rahman Khan & D.S. Poskitt, 2010. "Description Length Based Signal Detection in singular Spectrum Analysis," Monash Econometrics and Business Statistics Working Papers 13/10, Monash University, Department of Econometrics and Business Statistics.
  9. Telesca, Luciano & Lovallo, Michele & Shaban, Amin & Darwich, Talal & Amacha, Nabil, 2013. "Singular spectrum analysis and Fisher–Shannon analysis of spring flow time series: An application to Anjar Spring, Lebanon," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(17), pages 3789-3797.

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