Modern Methods for Fractal and Multifractal Analysis of Time Series: Theoretical Frameworks and Practical Applications

Dear Colleagues,

Fractal analysis of time series holds significant importance and finds wide-ranging applications across various domains today due to its ability to uncover hidden patterns and complex structures inherent in temporal data. It provides a powerful framework for capturing the long-term memory and scaling properties of time series, making it invaluable in understanding the underlying dynamics of diverse systems.

The Special Issue aims to explore the cutting-edge techniques employed and advancements made in analyzing time series data using self-similar and multifractal approaches. It delves into both the theoretical underpinnings and real-world applications of these methods, highlighting their relevance and potential impact in various scientific and practical domains.

This Special Issue serves as an essential resource for researchers, practitioners, and students interested in leveraging advanced techniques to analyze time series data. By combining theoretical foundations with diverse practical applications, this volume seeks to advance the understanding and utilization of fractal methods across various disciplines.

 The scope of this Special Issue includes, but is not limited to, the following topics:

• Theoretical concepts of fractal analysis applied to time series.
• Innovative methodologies and algorithms for evaluating self-similarity in time series.
• Time series multifractal analysis techniques.
• AI-enabled analysis of time series with fractal structure: forecasting, anomaly detection, clustering, classification, and others.
• AI-driven fractal analysis: exploring the synergy of artificial intelligence and fractal investigations.
• Applications: economics and finance; biomedical signal processing and enhanced medical diagnostics; study of anomalous diffusion; network traffic analysis; environmental and climate science; optimizing industrial processes; interdisciplinary research.

Prof. Dr. Lyudmyla Kirichenko
Guest Editor

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