Journal of Data Science· 2021Q3
Singular Spectrum Analysis: Methodology and Comparison
- 627citations
- Q3SCImago
- 2021year
Short summary
Singular Spectrum Analysis (SSA) provides more accurate time series forecasts than Box-Jenkins SARIMA, ARAR, and Holt-Winter methods, as demonstrated on US monthly accidental deaths data.
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Key points
- SSA was compared against Box-Jenkins SARIMA, ARAR, and Holt-Winter algorithms.
- The comparison used monthly accidental deaths data from the USA.
- SSA demonstrated superior forecasting accuracy over the other methods.
- The study highlights SSA as a powerful technique for time series analysis.
AI-generated from the title and abstract; the full text is not read.
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.
The authors' abstract, as published at the source. Journal of Data Science, 2021 · DOI ↗
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Field: Applied Mathematics
Applied MathematicsMathematics