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Topological recognition of critical transitions in time series of cryptocurrencies

Marian Gidea, Daniel Goldsmith, Yuri Katz, Pablo Roldan and Yonah Shmalo

Papers from arXiv.org

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.

New Economics Papers: this item is included in nep-big and nep-pay
Date: 2018-09
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