Hassani, Hossein (2007): Singular Spectrum Analysis: Methodology and Comparison. Published in: Journal of Data Science , Vol. 5, No. 2 (1. April 2007): pp. 239-257.
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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.
|Item Type:||MPRA Paper|
|Institution:||Cardiff University and Central Bank of the Islamic Republic of Iran|
|Original Title:||Singular Spectrum Analysis: Methodology and Comparison|
|Keywords:||ARAR algorithm; Box-Jenkins SARIMA models; Holt-Winter algorithm; singular spectrum analysis (SSA); USA monthly accidental deaths series|
|Subjects:||C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C14 - Semiparametric and Nonparametric Methods: General
C - Mathematical and Quantitative Methods > C6 - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling > C61 - Optimization Techniques; Programming Models; Dynamic Analysis
C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods; Simulation Methods
|Depositing User:||Hossein Hassani|
|Date Deposited:||22. Sep 2007|
|Last Modified:||07. Jan 2014 18:14|
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