Information-theoretical analysis of statistical measures for multiscale dynamics
Naoki Asuke, Tomoki Yamagami, Takatomo Mihana, Andr\'e R\"ohm, Ryoichi, Horisaki, Makoto Naruse

TL;DR
This paper explores the theoretical connections between multiscale entropy and Allan variance, showing they share foundational principles and exhibit similar properties in analyzing complex physical systems across multiple time scales.
Contribution
It reveals the information-theoretical relationship between MSE and Allan variance and demonstrates their similar behavior in real-world data, providing a unified understanding of multiscale analysis.
Findings
MSE and Allan variance show similar tendencies in biological and physical data.
The conditions for their consistency are related to specific conditional probabilities.
Artificial data can break the similarity, highlighting the conditions' importance.
Abstract
Multiscale entropy (MSE) has been widely used to examine nonlinear systems involving multiple time scales, such as biological and economic systems. Conversely, Allan variance has been used to evaluate the stability of oscillators, such as clocks and lasers, ranging from short to long time scales. Although these two statistical measures were developed independently for different purposes in different fields in the literature, their interest is to examine multiscale temporal structures of physical phenomena under study. We show that, from an information-theoretical perspective, they share some foundations and exhibit similar tendencies. We experimentally confirmed that similar properties of the MSE and Allan variance can be observed in low-frequency fluctuations (LFF) in chaotic lasers and physiological heartbeat data. Furthermore, we calculated the condition under which this consistency…
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Taxonomy
TopicsComplex Systems and Time Series Analysis · Chaos control and synchronization · Nonlinear Dynamics and Pattern Formation
