Multivariate Functional Singular Spectrum Analysis Over Different Dimensional Domains
This work addresses the analysis of multivariate functional time series for applications such as climate and remote sensing, but it is incremental as it extends existing methods to functional domains.
The authors developed Multivariate Functional Singular Spectrum Analysis (MFSSA), a functional extension of multivariate singular spectrum analysis, to tackle the problem of analyzing multivariate functional time series signals, achieving better reconstruction accuracy compared to other approaches in simulations and demonstrating enriched analysis with real-world data like temperature curves and remote sensing images.
In this work, we develop multivariate functional singular spectrum analysis (MFSSA) over different dimensional domains which is the functional extension of multivariate singular spectrum analysis (MSSA). In the following, we provide all of the necessary theoretical details supporting the work as well as the implementation strategy that contains the recipes needed for the algorithm. We provide a simulation study showcasing the better performance in reconstruction accuracy of a multivariate functional time series (MFTS) signal found using MFSSA as compared to other approaches and we give a real data study showing how MFSSA enriches analysis using intraday temperature curves and remote sensing images of vegetation. MFSSA is available for use through the Rfssa R package.