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 tech nique has been considered by applying it to a well-known time series data set, namely, monthly accidental deaths in the USA. The results are com pared 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.
Highlights
The Singular Spectrum Analysis (SSA) technique is a novel and powerful technique of time series analysis incorporating the elements of classical time series analysis, multivariate statistics, multivariate geometry, dynamical systems and signal processing
The values Mean Absolute Error (MAE) and Mean Relative Absolute Error (MRAE) show the performance of forecasting
This paper has illustrated that the SSA technique performs well in the simultaneous extraction of harmonics and trend components
Summary
The Singular Spectrum Analysis (SSA) technique is a novel and powerful technique of time series analysis incorporating the elements of classical time series analysis, multivariate statistics, multivariate geometry, dynamical systems and signal processing. Hossein Hassani of complex trends and periodicities; 7) finding structure in short time series; and 8) change-point detection. Solving all these problems corresponds to the basic capabilities of SSA. The SSA method should be applied to time series governed by linear recurrent formulae to forecast the new data points. The method of change-point detection described in Moskvina and Zhigljavsky (2003) is based on the sequential application of SSA to subseries of the original series and monitors the quality of the approximation of the other parts of the series by suitable approximates. In this paper we start with a brief description of the methodology of SSA and finish by appliying this technique to the original series, namely, the monthly accidental deaths in the USA (Death series) and comparing the SSA technique with several other methods for forecasting results
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