Abstract
Cryptocurrencies are fungible digital assets whose market capitalization has not stopped growing since the appearance of their first use case in 2009, Bitcoin. However, one of the biggest problems facing cryptocurrencies is the enormous fluctuation of their value in the market. To help understand different patterns in cryptocurrency ecosystems, several machine learning-based solutions have been proposed in the literature. This paper aims to study in detail the solutions proposed in the literature for the detection of patterns and anomalies in cryptocurrency ecosystems. The aim is to bring together different proposals and studies to help users of this market to understand how it works.
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Acknowledgement
The research of Yeray Mezquita is supported by the pre-doctoral fellowship from University of Salamanca and co-funded by Banco Santander. This research was also partially Supported by the project “Computación cuántica, virtualización de red, edge computing y registro distribuido para la inteligencia artificial del futuro”, Reference: CCTT3/20/SA/0001, financed by Institute for Business Competitiveness of Castilla y León, and the European Regional Development Fund (FEDER).
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Mezquita, Y., Gil-González, A.B., Prieto, J., Corchado, J.M. (2022). Cryptocurrencies and Price Prediction: A Survey. In: Prieto, J., Partida, A., Leitão, P., Pinto, A. (eds) Blockchain and Applications. BLOCKCHAIN 2021. Lecture Notes in Networks and Systems, vol 320. Springer, Cham. https://doi.org/10.1007/978-3-030-86162-9_34
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DOI: https://doi.org/10.1007/978-3-030-86162-9_34
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