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Singular Spectrum Analysis: Methodology and Comparison

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Author Info
Hassani, Hossein

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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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File URL: http://mpra.ub.uni-muenchen.de/4991/
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Publisher 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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Related research
Keywords: ARAR algorithm Box-Jenkins SARIMA models Holt-Winter algorithm singular spectrum analysis (SSA) USA monthly accidental deaths series.

Find related papers by JEL classification:
C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Semiparametric and Nonparametric Methods
C61 - Mathematical and Quantitative Methods - - Mathematical Methods and Programming - - - Optimization Techniques; Programming Models; Dynamic Analysis
C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Other Model Applications

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