Topological recognition of critical transitions in time series of cryptocurrencies

We analyze four major cryptocurrencies (Bitcoin, Ethereum, Litecoin, and Ripple) before the digital asset market crash at the beginning of 2018. We also analyze Bitcoin before some of the mini-crashes that occurred during the period 2016–2018. All relevant time series exhibited a highly erratic beha...

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Bibliographic Details
Published in:Physica A Vol. 548; p. 123843
Main Authors: Gidea, Marian, Goldsmith, Daniel, Katz, Yuri, Roldan, Pablo, Shmalo, Yonah
Format: Journal Article
Language:English
Published: Elsevier B.V 15.06.2020
Subjects:
[formula omitted]-means clustering
Complex systems dynamics
Critical transitions
Cryptocurrency
Financial time series
Topological data analysis
k-means clustering
Critical transitions
Cryptocurrency
Topological data analysis
Financial time series
Complex systems dynamics
ISSN:0378-4371, 1873-2119
Online Access:Get full text
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Summary:We analyze four major cryptocurrencies (Bitcoin, Ethereum, Litecoin, and Ripple) before the digital asset market crash at the beginning of 2018. We also analyze Bitcoin before some of the mini-crashes that occurred during the period 2016–2018. All relevant time series exhibited a highly erratic behavior. We introduce a methodology that combines topological data analysis with a machine learning technique – k-means clustering – in order to characterize 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. Then we apply it to the four major cryptocurrencies. We find early warning signals for critical transitions, i.e., crashes, in the cryptocurrency markets. •We use persistence homology and clustering to detect critical transitions.•Our approach can be applied to strongly non-linear and non-stationary time series.•We detect early warning signals for crashes in the cryptocurrency market.
ISSN:0378-4371
1873-2119
DOI:10.1016/j.physa.2019.123843