Singular Spectrum Analysis: Methodology and Comparison

By Hossein Hassani. Journal of Data Science, 2021

394 citations recorded by the source corpus.

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.

Subfields: Applied Mathematics

View Paper: https://www.semanticscholar.org/paper/3ad99f3166e4bc2eda32e79d2b7399c274ee5fc0

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In Applied Mathematics

1. Shuang Yi
2. N. Sneeuw

Citations in the corpus

• Filling the Data Gaps Within GRACE Missions Using Singular Spectrum Analysis (2021, background): “It is widely used to extract the (non-linear) trend and leading oscillations of time signals or to eliminate noise (Hassani, 2007).”