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arXiv · 2411.14999

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

Catalina Lesmes·Francisco Zuluaga·Henry Laniado·Andres Gomez·Andrea Carvajal
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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.

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Catalina Lesmes, Francisco Zuluaga, Henry Laniado, Andres Gomez, Andrea Carvajal. 2024-11-22. The EE-Classifier: A classification method for functional data based on extremality indexes. https://arxiv.org/abs/2411.14999

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