[Forum SIS] seminar: Seyed Morteza Najibi "Functional Singular Spectrum Analysis with application to remote sensing data", 8 June

[Umberto Picchini]

You are welcome to the next Statistics seminar at Dept of Mathematical 
Sciences at Chalmers and Göteborg University, Sweden.

We are very glad to have, on Tuesday 8 June, Seyed Morteza Najibi 
<https://www.smnajibi.com/> (Lund University) who will talk on

/*Functional Singular Spectrum Analysis with application to remote 
sensing data */
*//*

Zoom:https://chalmers.zoom.us/j/69382451762
Password: 621424

When: 14.15-15.15 Swedish time,  8 June.

Feel free to circulate this invitation in your network.

*​Abstract *
*
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.

About the speaker
**
Seyed Morteza Najibi <https://www.smnajibi.com/> is a research fellow in 
Statistical Machine Learning at Lund University (Sweden). He researches 
probabilistic models to combine computational and experimental methods 
in protein modeling and prediction. Other specific interests include 
directional statistics, Bayesian modeling and non-parametrics.

*

-- 
_________________________________________________________
Umberto Picchini, Associate Professor, PhD, Docent
https://umbertopicchini.github.io/  , twitter: @uPicchini

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