EPJ ST Special Issue: Framework of Fractals in Data Analysis: Theory and Interpretation

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Published on 30 June 2022

The analysis and reconstruction of real world data is a fundamental and important metric in decision-making. Most of the real data exhibits irregularity or complex features when they are plotted graphically. The concepts of fractal geometry are introduced to offer new visual conceptions for real-world objects with roughness, that cannot be perceived using Euclidean geometry. In recent years, there has been a surge of interest in the field of fractal analysis, as it provides more sophisticated and flexible approaches in the areas of data analysis and approximation, such as estimating fractal dimension, multifractal analysis and methods of fractal interpolation.

Fractal geometry offers a finest tool namely, Fractal Dimension (FD) for describing any time series with fractal nature and it is often used a measure of data complexity. It has also been used to investigate the future dynamics of several types of times series signals as a predictability indicator. Fractal Interpolation Functions (FIFs) are a class of functions introduced in relation to the theory of classical interpolation and approximation to approximate the irregular behaviour of natural phenomena in a way analogous to classical interpolation functions. The modest purpose of fractal functions is to aid in the reconstruction of fragmented data or in the appropriate construction of missing data in a low data rate sampling situations.

The purpose of this special issue is to collect articles which propose robust fractal theories to address and analyse the complexity of real data under the topic “Framework of Fractals in Data Analysis: Theory and Interpretation”. Emerging researchers are appreciatively invited to submit original research articles on any of the potential topics listed below, but not limited to:

• Fractal time series
• Fractal dimension and fractal wavelets on economic, biomedical and industrial time series
• Fractal and multifractal in in Big data analysis
• Parameter identification problems in signal and image processing
• Fractals, multifractal, percolations and scaling law for financial data
• Fractal based preprocessing and prediction models in the geophysical data, meteorological data and analysis of climate changes
• Image encryption, compression via iterated function system or fractal reconstruction methods
• Fractal reconstruction of multi-dimensional signals
• New theory on various fractal interpolation function with applications in data analysis
• Fractals in machine learning

The Guest Editors invite authors to submit their original research and short reviews on the theme of the Special Issue of the European Physical Journal - Special Topics. Articles may be one of four types: (i) minireviews (10-15 pages), (ii) tutorial reviews (15+ pages), (iii) original paper v1 (5-10 pages), or (iv) original paper v2 (3-5 pages). More detailed descriptions of each paper type can be found in the Submission Guidelines. Manuscripts should be prepared using the latex template (preferably 2-column layout), which can be downloaded here.

Articles should be submitted to the Editorial Office of EPJ ST via the submission system, and should be clearly identified as intended for the topical issue “Framework of Fractals in Data Analysis”.

Guest Editors

Santo Banerjee, Dipartimento di Scienze Matematiche, Politecnico di Torino, Corso Duca degli Abruzzi, 24, 10129, Torino, Italy, This email address is being protected from spambots. You need JavaScript enabled to view it.

Gowrisankar Arulprakash, Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Vellore, Tamil Nadu, India, This email address is being protected from spambots. You need JavaScript enabled to view it.

Open Access: EPJST is a hybrid journal offering Open Access publication via the Open Choice programme and a growing number of Springer Compact “Publish and Read” arrangements which enable authors to publish OA at no direct cost (all costs are paid centrally).