# The Topological Stability Index: A Variance-Based Measure for Persistence Barcodes

arXiv: https://arxiv.org/abs/2605.29839 · HTML: https://mlsanity.com/paper/2605.29839/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 19 references that are in the map.
- First posted on arXiv 28 May 2026.

## Builds on (most cited first, 5 of 5)

- [Persistence Curves: A canonical framework for summarizing persistence diagrams](https://mlsanity.com/paper/1904.07768/) (2019)
- [Robust Topological Inference: Distance To a Measure and Kernel Distance](https://mlsanity.com/paper/1412.7197/) (2014)
- [DTM-based Filtrations](https://mlsanity.com/paper/1811.04757/) (2018)
- [Hypothesis Testing for Topological Data Analysis](https://mlsanity.com/paper/1310.7467/) (2013)
- [The Shape of Data: Topology Meets Analytics. A Practical Introduction to Topological Analytics and the Stability Index (TSI) in Business](https://mlsanity.com/paper/2511.13503/) (2025)

## Abstract (arXiv)

We introduce the Topological Stability Index (TSI), a variance-based scalar measure for persistence barcodes that quantifies the dispersion of persistence lifetimes. Unlike persistent entropy, which depends only on normalized weights, the TSI captures absolute variability and is sensitive to heterogeneous feature scales. We establish fundamental properties of the TSI, including its scaling behavior, invariance under lifetime translation and explicit update formulas under insertion and deletion of bars. We also consider a complementary first-moment-type quantity, the Topological Signal Index (TSigI), which captures the typical scale of persistence lifetimes and provides additional interpretability alongside the TSI. We further introduce a normalized version, cvTSI, which is scale invariant and admits an explicit algebraic relation to the R'enyi entropy of order two. In particular, cvTSI is an affine function of the collision probability sumi pi2, and therefore a monotone reparametrization of the R'enyi entropy, providing a direct link between variance-based and entropy-based summaries in topological data analysis. Numerical experiments on synthetic data and stochastic time series demonstrate that the TSI captures structural variability complementary to entropy: it is relatively insensitive to deterministic trends, while responding strongly to stochastic fluctuations and variations in persistence magnitude.