# A proposal of a methodological framework with experimental guidelines to investigate clustering stability on financial time series

arXiv: https://arxiv.org/abs/1509.05475 · HTML: https://mlsanity.com/paper/1509.05475/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Sep 2015.

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

- [Forecasting Financial Market Structure from Network Features using Machine Learning](https://mlsanity.com/paper/2110.11751/) (2021)

## Abstract (arXiv)

We present in this paper an empirical framework motivated by the practitioner point of view on stability. The goal is to both assess clustering validity and yield market insights by providing through the data perturbations we propose a multi-view of the assets' clustering behaviour. The perturbation framework is illustrated on an extensive credit default swap time series database available online at www.datagrapple.com.