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Unifying concepts in information-theoretic time-series analysis
Annie G Bryant1,2, Oliver M Cliff1,2, James M Shine2,3
1School of Physics, The University of Sydney, Sydney, New South Wales, Australia.
Journal of the Royal Society, Interface
|May 19, 2026
Summary
This study unifies information theory measures for time-series analysis, standardizing definitions and visualizations. This framework enhances interdisciplinary research in complex systems like neuroscience.
Area of Science:
- Complex Systems Analysis
- Computational Neuroscience
- Information Theory
Background:
- Information theory quantifies complexity in time-series data across various scientific fields.
- Existing literature is fragmented by inconsistent terminology, notation, and visualization, hindering interdisciplinary integration.
Purpose of the Study:
- To unify key information-theoretic time-series measures.
- To standardize semantic definitions, mathematical notation, and visual representations.
- To facilitate interdisciplinary understanding and application of these measures.
Main Methods:
- Developed a unified framework for information-theoretic time-series measures.
- Standardized mathematical notation and semantic definitions.
- Utilized a case study with functional magnetic resonance imaging (fMRI) data.
Main Results:
- Demonstrated the complementary insights offered by unified measures in characterizing neural system dynamics.
- Showcased applications in analyzing signal complexity and information flow.
- Provided a common conceptual space for comparing different measures.
Conclusions:
- The unified framework enhances interdisciplinary dialogue and methodological adoption in computational neuroscience.
- Offers a valuable resource for researchers applying information-theoretic measures to complex systems.
- Improves accessibility and reproducibility in the analysis of time-series data.
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