
arXiv: 2006.03933
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.
28 pages for the paper, 15 pages for the supplementary material, 8 figures total
Methodology (stat.ME), FOS: Computer and information sciences, Statistics - Machine Learning, Machine Learning (stat.ML), 62H35, 62M15, 46H30, 62M10, Statistics - Methodology
Methodology (stat.ME), FOS: Computer and information sciences, Statistics - Machine Learning, Machine Learning (stat.ML), 62H35, 62M15, 46H30, 62M10, Statistics - Methodology
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
