functional singular spectrum analysis

In this paper, we introduce a new extension of the singular spectrum analysis (ssa) called functionalssa to analyze functional time series. the new methodology is developed by integrating ideas fromfunctional data analysis and univariate ssa. we explore the advantages of the functional ssa in terms ofsimulation results and two real data applications. we compare the proposed approach with multivariatessa (mssa) and dynamic functional principal component analysis (dfpca). the results suggest thatfurther improvement to mssa is possible, and the new method provides an attractive alternative to thedfpca approach that is used for analyzing correlated functions. we implement the proposed techniqueto an application of remote sensing data and a call center dataset. we have also developed an ecientand user-friendly r package and a shiny web application to allow interactive exploration of the results.