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

• Grant number: 21/05136-4
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• Start date: September 01, 2021
• End date: August 31, 2023
• Field of knowledge: Physical Sciences and Mathematics - Probability and Statistics - Statistics
• Principal Investigator: Ronaldo Dias
• Grantee: Olushina Olawale Awe
• Host 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 significance 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)