# Functional Singular Spectrum Analysis

## TL;DR

This paper introduces functional SSA, a novel method for analyzing functional time series by combining functional data analysis with univariate SSA, demonstrated through simulations and real-world applications.

## Contribution

The paper presents a new functional SSA method that extends traditional SSA to functional data, offering an alternative to MSSA and dFPCA with practical implementation tools.

## Key findings

- Functional SSA outperforms MSSA in certain scenarios.
- The method provides a competitive alternative to dFPCA.
- An R package and web app facilitate practical use.

## Abstract

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.