Some Thoughts on Symbolic Transfer Entropy

TL;DR

This paper examines the embedding dimension parameter in symbolic transfer entropy, proposing optimization methods for complex data and offering perspectives on transfer entropy estimation techniques.

Contribution

It introduces optimization approaches for the embedding dimension in symbolic transfer entropy and discusses alternative estimation methods.

Findings

Optimized embedding dimension selection improves transfer entropy estimation.

Proposed methods enhance robustness in complex, high-dimensional data.

Provides insights into alternative transfer entropy estimation techniques.

Abstract

Transfer entropy is used to establish a measure of causal relationships between two variables. Symbolic transfer entropy, as an estimation method for transfer entropy, is widely applied due to its robustness against non-stationarity. This paper investigates the embedding dimension parameter in symbolic transfer entropy and proposes optimization methods for high complexity in extreme cases with complex data. Additionally, it offers some perspectives on estimation methods for transfer entropy.