Singular spectrum analysis based on the minimum variance estimator

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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 SSA technique based on the minimum variance estimator is introduced. The SSA technique based on the minimum variance and least squares estimators in reconstructing and forecasting time series is also considered. A well-known time series data set, namely, monthly accidental deaths in the USA time series, is used in examining the performance of the technique. The results are compared with several classical methods namely, Box–Jenkins SARIMA models, the ARAR algorithm and the Holt–Winter algorithm.