I know that Wavelet transform Coherence is the most common method to find the linkage between two time series, in field of finance.
- Can we use Singular Spectrum Analysis to find out the linkage of two time series?
- If it's possible so what is the disadvantages of Singular Spectrum Analysis that most of researchers use Wavelet transform coherence?
Andreas Groth
Singular Spectrum analysis will help you identify similar spectral components in two or more time series that could be interpreted as linkage between them.
- While Wavelet coherence considers two time series, Singular Spectrum Analysis can be generalized to many more time series. A paper that studies synchronization (linkage) of time series by means of singular spectrum analysis could be helpful to better understand this property:
Article Multivariate singular spectrum analysis and the road to phas...
- An application that compares Wavelet coherence with Singular Spectrum Analysis could likewise further give some helpful insights:
Article Pathogens trigger top-down climate forcing on ecosystem dynamics
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Bernard Cazelles
- Singular spectrum analysis (SSA) is mainly driven by the data and wavelet coherence could be influenced by the choice of the mother wavelet function.
- Multivariate singular spectrum analysis generalized SSA to multiple time series but now this generalization is also possible with wavelet analysis and the use of generalized wavelet coherence (see Chavez, M., & Cazelles, B. (2019). Detecting dynamic spatial correlation patterns with generalized wavelet coherence and non-stationary surrogate data. Scientific reports, 9(1), 7389.)
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Jaya Krishna Sunkara
Check this.
https://elcvia.cvc.uab.es/article/view/v18-n1-sunkara
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