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Real-Time Anomaly Prediction from Cryptocurrency Time Series

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Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2135))

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Abstract

Cryptocurrencies have gained popularity for their decentralized nature and ability to facilitate secure cross-border transactions, but they are susceptible to market volatility and anomalies. This extended abstract introduces a new real-time anomaly prediction method for cryptocurrency time series, that exploits temporal correlation. The proposed approach analyzes the temporal correlation between similar cryptocurrencies to identify clusters exhibiting similar patterns that may provide insights about future anomalies. Subsequently, an online multi-target LSTM model is adopted to predict upcoming anomaly events. Our preliminary experiments on 17 real-world cryptocurrency demonstrated the potential of the proposed approach for detecting anomalies and improving trading strategies in the cryptocurrency market.

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Notes

  1. 1.

    https://www.alternative.me/crypto/fear-and-greed-index.

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Acknowledgements

This work was partially supported by the project FAIR - Future AI Research (PE00000013), Spoke 6 - Symbiotic AI, under the NRRP MUR program funded by the NextGenerationEU.

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Correspondence to Gianvito Pio .

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Pellicani, A., Pio, G., Deroski, S., Ceci, M. (2025). Real-Time Anomaly Prediction from Cryptocurrency Time Series. In: Meo, R., Silvestri, F. (eds) Machine Learning and Principles and Practice of Knowledge Discovery in Databases. ECML PKDD 2023. Communications in Computer and Information Science, vol 2135. Springer, Cham. https://doi.org/10.1007/978-3-031-74633-8_42

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  • DOI: https://doi.org/10.1007/978-3-031-74633-8_42

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  • Print ISBN: 978-3-031-74632-1

  • Online ISBN: 978-3-031-74633-8

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