---
title: "Clustering Financial Time Series: How Long Is Enough?"
authors:
  - "Gautier Marti"
  - "Sébastien Andler"
  - "Frank Nielsen"
  - "Philippe Donnat"
doi: ""
url: "https://ijcai.org/proceedings/2016/367"
pdf: "https://ijcai.org/proceedings/2016/0367.pdf"
venue: "Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence"
year: 2016
pages: "2583-2588"
track: ""
keywords:
---

# Clustering Financial Time Series: How Long Is Enough?

Gautier Marti, Sébastien Andler, Frank Nielsen, Philippe Donnat

*Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence*, pages 2583-2588.

## Abstract

Researchers have used from 30 days to several years of daily returns as source data for clustering financial time series based on their correlations. This paper sets up a statistical framework to study the validity of such practices. We first show that clustering correlated random variables from their observed values is statistically consistent. Then, we also give a first empirical answer to the much debated question: How long should the time series be? If too short, the clusters found can be spurious; if too long, dynamics can be smoothed out.