Singular Spectrum Analysis (SSA) is a non-parametric method that can be applied to analyze time series of complex structure. The main purpose of SSA is a decomposition of the original series into a sum of series, so that each component in this sum can be identified as either a trend, periodic or quasi-periodic component (perhaps, amplitude modulated), or noise. In this paper, by means of statistical simulation, we compare the performance of the SSA and ARIMA models. We show that, in general, the performance of forecasting using these methods are different and depends on parameters of the models.
Keywords: SSA; ARIMA; Forecasting
Biography: Mr Rahim Mahmoudvand is a Ph.D student at Shahid Beheshti University of Iran. His interesting topics are: Modelling and application of statistic in other feilds such as insurance, economic, engineering and so on.