Development of Novel Techniques and Original Applications in the Functional Data Analysis Framework

Dear Colleagues,

In recent decades, technological progress has led to the development of instruments to gather vast amounts of data, usually recorded at temporal stamps or arriving over time, e.g., data from sensors. For this reason, statistical and IT techniques are needed to process these data appropriately. In the statistical, mathematical, and computer science literature, particularly in the last fifteen years, research in Functional Data Analysis (FDA) has become very lively for dealing with this data type. The basic idea of FDA is that the objects of interest are the functions rather than the scalars; indeed, FDA focuses on curves, surfaces, or anything else varying over a continuum. The advantages of the FDA are many because, as highlighted by many researchers, this approach allows for reducing the dimensionality of the data and obtaining additional information, such as that contained in the derivatives, curvature, and other characteristics of the curves.

Furthermore, the functional approach does not require any particular assumptions and is well suited to deal with time series and other functions in a domain other than time. Research has progressed in many study contexts in recent years, such as supervised and unsupervised classification, regression, outlier detection, and functional time series. However, this research field requires much attention because many developments are still possible.

The topics of interest include, but are not limited to:

• Functional supervised classification;
• Functional clustering;
• Smoothing technique;
• Functional dimensionality reduction;
• Functional regression;
• Functional analysis of variance;
• Functional outliers’ detection;
• Functional time series.

The Special Issue welcomes novel techniques and applied contributions demonstrating the potential and challenges of FDA methods and models in solving real-world problems.

Dr. Fabrizio Maturo
Dr. Elvira Romano
Guest Editors

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