Determining Optimal Time Delay for Chaotic Sequences Using Mutual Information Method
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This paper presents a mutual information-based approach for determining optimal time delays in chaotic sequences. Grounded in information theory, this method is widely adopted in signal processing and time series analysis. We elaborate on the mathematical principles and implementation steps, including key algorithmic components such as probability distribution estimation and entropy calculations. The implementation typically involves partitioning phase space into bins to compute joint and marginal probabilities, followed by mutual information quantification using logarithmic operations. Practical code examples demonstrate how to automate delay selection by identifying the first minimum of the mutual information function. This approach serves as a valuable tool for researchers analyzing chaotic systems, enhancing both theoretical understanding and practical application in complex dynamics analysis.
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