Mehdi Maadooliat, PhD, will give the sixth talk in The USM Data Science Ensemble, a seminar series focused on the intersection of data science and real-world applications. We invite you to join us for this in-depth look at a practical application of data science in the real world, by joining this moderated Zoom link.
In Dr. Maadooliat's talk, "Functional Singular Spectrum Analysis," he will introduce a new extension of the Singular Spectrum Analysis (SSA) called functional SSA to analyze functional time series. The new methodology has been developed by integrating ideas from functional data analysis and univariate SSA. We will explore the advantages of the functional SSA in terms of simulation results and two real data applications. We will 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 will 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.
Mehdi Maadooliat is a faculty member of the Department of Mathematical and Statistical Sciences at Marquette University. Mehdi was also affiliated with Marshfield Clinic Research Institute as Associate Research Scientist from 2015 to 2020. He received his B.Sc. from the Sharif University of Technology, M.Sc. from Marquette, and a Ph.D. degree in Statistics from Texas A&M University, where he also served as a post-doctoral fellow. His primary research interests include machine learning, bioinformatics, and functional data analysis. Recently he is working on developments of the statistical models in high-dimensional data structures with application to biological sciences, including but not limited to genomics and proteomics.