Topological recognition of critical transitions in time series of cryptocurrencies

TL;DR

This paper presents a novel approach combining topological data analysis and machine learning to detect early warning signals of critical transitions in cryptocurrency markets, demonstrating effectiveness despite chaotic behavior.

Contribution

It introduces a new methodology integrating topological data analysis with k-means clustering to identify critical transitions in complex time series.

Findings

Early warning signals detected before market crashes

Method effective on chaotic and erratic data

Applicable to multiple cryptocurrencies

Abstract

We analyze the time series of four major cryptocurrencies (Bitcoin, Ethereum, Litecoin, and Ripple) before the digital market crash at the end of 2017 - beginning 2018. We introduce a methodology that combines topological data analysis with a machine learning technique -- $k$-means clustering -- in order to automatically recognize the emerging chaotic regime in a complex system approaching a critical transition. We first test our methodology on the complex system dynamics of a Lorenz-type attractor, and then we apply it to the four major cryptocurrencies. We find early warning signals for critical transitions in the cryptocurrency markets, even though the relevant time series exhibit a highly erratic behavior.