The EE-Classifier: A classification method for functional data based on extremality indexes

TL;DR

This paper introduces the EE-Classifier, an extremality index-based method for supervised classification of functional data, demonstrating its effectiveness on real and synthetic datasets, including financial data analysis.

Contribution

It presents a novel extremality-based classifier for functional data, extending existing depth-based methods with improved classification performance.

Findings

Accurately classifies functional data in various datasets.

Effective in analyzing stock market fluctuations.

Outperforms some existing classification methods.

Abstract

Functional data analysis has gained significant attention due to its wide applicability. This research explores the extension of statistical analysis methods for functional data, with a primary focus on supervised classification techniques. It provides a review on the existing depth-based methods used in functional data samples. Building on this foundation, it introduces an extremality-based approach, which takes the modified epigraph and hypograph indexes properties as classification techniques. To demonstrate the effectiveness of the classifier, it is applied to both real-world and synthetic data sets. The results show its efficacy in accurately classifying functional data. Additionally, the classifier is used to analyze the fluctuations in the S\&P 500 stock value. This research contributes to the field of functional data analysis by introducing a new extremality-based classifier. The successful application to various data sets shows its potential for supervised classification tasks and provides valuable insights into financial data analysis.