  In this paper, we introduce a new extension of the Singular Spectrum Analysis
(SSA) called functional SSA to analyze functional time series. The new
methodology is developed by integrating ideas from functional data analysis and
univariate SSA. We explore the advantages of the functional SSA in terms of
simulation results and two real data applications. We compare the proposed
approach with Multivariate SSA (MSSA) and dynamic Functional Principal
Component Analysis (dFPCA). The results suggest that further improvement to
MSSA is possible, and the new method provides an attractive alternative to the
dFPCA approach that is used for analyzing correlated functions. We implement
the proposed technique to an application of remote sensing data and a call
center dataset. We have also developed an efficient and user-friendly R package
and a shiny web application to allow interactive exploration of the results.
