Manuscript Topics

Applied Functional Data Analysis (FDA) is a statistical branch focused on analyzing and interpreting data represented as functions or curves, rather than traditional scalar or vector observations. This approach excels at managing complex, high-dimensional data that vary over continuous domains such as time, space, or wavelengths. FDA emerged to effectively analyze data naturally indexed over a continuum, where traditional statistical methods often fall short in capturing the intricacies of the data. FDA provides a robust framework to model and analyze this type of data, offering a more nuanced understanding of its underlying patterns and structures. In recent years, substantial advances in applied FDA have been made.

The special issue, titled “Applied Functional Data Analysis”, aims to provide a comprehensive overview of the latest advancements and applications in FDA. It highlights innovative methodologies and their practical implications in analyzing complex datasets that evolve continuously over dimensions such as time, space, or other functional domains. Key areas of focus include the development of FDA models, the integration of FDA with modern machine learning techniques, and the exploration of its applications in diverse fields such as bioinformatics, finance, and environmental studies. By compiling a collection of studies and case analyses, this issue endeavors to enhance the practical understanding and capabilities of FDA, addressing challenges in data interpretation and predictive modeling. We invite original and high-quality contributions on this subject. The topics of interest include, but are not limited to, the keywords listed below.

Keywords: applied functional data analysis; FDA in machine learning integration, bioinformatics, finance, and environmental studies

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