# Transfer entropy for finite data

arXiv: https://arxiv.org/abs/2506.16215 · HTML: https://mlsanity.com/paper/2506.16215/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jun 2025.

## Built on by (most cited first, 1 of 1)

- [Scalable inference of spatial regions and temporal signatures from time series](https://mlsanity.com/paper/2605.05008/) (2026)

## Abstract (arXiv)

Transfer entropy is a widely used measure for quantifying directed information flows in complex systems. While the challenges of estimating transfer entropy for continuous data are well known, it has two major shortcomings for data of finite cardinality: it exhibits a substantial positive bias for sparse bin counts, and it has no clear means to assess statistical significance. By computing information content in finite data streams without explicitly considering symbols as instances of random variables, we derive a transfer entropy measure which is asymptotically equivalent to the standard plug-in estimator but remedies these issues for time series of small size and/or high cardinality, permitting a fully nonparametric assessment of statistical significance without simulation.