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Robust singular spectrum analysis, classification, dimension reduction of functional data with applications to climate change and epidemiological data

Grant number: 21/05136-4
Support type:Scholarships in Brazil - Post-Doctorate
Effective date (Start): September 01, 2021
Effective date (End): August 31, 2023
Field of knowledge:Physical Sciences and Mathematics - Probability and Statistics - Statistics
Principal researcher:Ronaldo Dias
Grantee:Olushina Olawale Awe
Home Institution: Instituto de Matemática, Estatística e Computação Científica (IMECC). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
Associated research grant:18/04654-9 - Time series, wavelets and high dimensional data, AP.TEM

Abstract

Time series forecasting has attained high dimensions of signicance in management, planning, and decision making for government institutions, industries, and businesses. However, the forecasting implementations are usually limited due to various factors such as data complexity, environmental conditions, economic variables, risk situations, among others, which originates from highly dynamic systems; and consequently make data analysis become complex, thereby producing inaccurate results. I propose a new researchactivity will cover two broad topics which will include the study of robust Singular Spectrum Analysis (SSA)and robust Principal Component Analysis (PCA) for clustering/forecasting high-dimensional, multivariable, functional and large datasets with several medical and environmental applications. Data would be collected specically for the BRICS nations and the use of wavelet transformations as a dimensionality reduction technique to permit ecient similarity searching in high-dimensional time-series. (AU)

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