moving average

functional data

computationally efficient

forecasts

exponential smoothing

series analysis

series models

Journal of data science

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.

Singular Spectrum Analysis: Methodology and Comparison