We propose an exploratory statistical tool that combines functional data analysis (FDA) with unsupervised learning algorithms and spatial statistics to extract meaningful information about the main spatiotemporal patterns underlying air pollutant exceedances in mainland Portugal. Firstly, we describe the temporal evolution of air pollutant concentrations by CAMS grid node as a function of time and outline the main temporal patterns of variability using a functional principal component analysis. Then, CAMS grid nodes are classified according to their spatiotemporal similarities through hierarchical clustering adapted to spatially correlated functional data. The proposed methodology provides an automated and robust approach for quantifying temporal trends in PM10 time series data to support subsequent air quality classification.

We propose an exploratory statistical tool that combines functional data analysis (FDA) with unsupervised learning algorithms and spatial statistics to extract meaningful information about the main spatiotemporal patterns underlying PM10 in mainland Portugal.

This study applies FDA to examine drought conditions in Southern Portugal using the Consecutive Dry Days (CDD) index. By integrating the FDA with spatial analyses, the research aims to provide a comprehensive understanding of drought dynamics, revealing detailed patterns of drought persistence, seasonal behavior, and spatial distribution.

Alguns exemplos com funções que implementam a validação cruzada para a análise discriminante.

Gráficos e tabelas para análise descritiva de dados quantitativos e categóricos

exercicio com kriging da indicatriz onde ocorre screen-effect

Continuidade espacial e anisotropia geometrica