Functional Singular Spectrum Analysis with application to remote sensing data

Description

One of the popular approaches in the decomposition of time series is accomplished using the rates of change. In this approach, the observed time series is partitioned (decomposed) into informative trends plus potential seasonal (cyclical) and noise (irregular) components. Aligned with this principle, Singular Spectrum Analysis (SSA) is a model-free procedure that is commonly used as a nonparametric technique in analyzing the time series. SSA does not require restrictive assumptions such as stationarity, linearity, and normality. It can be used for a wide range of purposes such as trend and periodic component detection and extraction, smoothing, forecasting, change-point detection, gap filling, causality, and so on.
In this talk, I will briefly overview SSA methodology and introduce a new extension called functional SSA to analyze functional time series. This is developed by integrating ideas from functional data analysis and univariate SSA. I will demonstrate this approach for tracking changes in vegetation over time by analyzing the kernel density functions of Normalized Difference Vegetation Index (NDVI) images. At the end of the talk, I will also illustrate a simulated example in the interactive Shiny web application implemented in the Rfssa package.

Period	2021 Jun 8
Held at	Chalmers University of Technology, Sweden
Degree of Recognition	National