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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References
Awoke, T., Rout, M., Mohanty, L., Satapathy, S.C.: Bitcoin price prediction and analysis using deep learning models. In: Communication Software and Networks, pp. 631–640. Springer (2021)
Bhutta, M.N.M., et al.: A survey on blockchain technology: evolution, architecture and security. IEEE Access 9, 61048–61073 (2021)
Ceci, M., Impedovo, A., Pellicani, A.: Leveraging multi-target regression for predicting the next parallel activities in event logs. In: ECML PKDD 2020 Workshops, pp. 237–248. Springer, Cham (2020)
Chen, Z., Li, C., Sun, W.: Bitcoin price prediction using machine learning: an approach to sample dimension engineering. J. Comput. Appl. Math. 365, 112395 (2020)
Fama, E.F.: Efficient capital markets: a review of theory and empirical work. J. Financ. 25(2), 383–417 (1970)
Haber, S., Stornetta, W.S.: How to time-stamp a digital document. In: Conference on the Theory and Application of Cryptography, pp. 437–455. Springer (1990)
Hasan, S.H., et al.: A novel cryptocurrency prediction method using optimum CNN. Comput. Mater. Continua 71(1), 1051–1063 (2022)
Kaufman, L., Rousseeuw, P.J.R.: Partitioning Around Medoids (Program PAM), chap. 2, pp. 68–125. Wiley (1990)
Ko, J.U., Na, K., Oh, J.S., Kim, J., Youn, B.D.: A new auto-encoder-based dynamic threshold to reduce false alarm rate for anomaly detection of steam turbines. Expert Syst. Appl. 189, 116094 (2022)
Lahmiri, S., Bekiros, S.: Cryptocurrency forecasting with deep learning chaotic neural networks. Chaos Solitons Fractals 118, 35–40 (2019)
de Oliveira Carosia, A.E., Coelho, G.P., da Silva, A.E.A.: Investment strategies applied to the Brazilian stock market: a methodology based on sentiment analysis with deep learning. Exp. Syst. Appl. 184, 115470 (2021)
Valle-Cruz, D., et al.: Does Twitter affect stock market decisions? Financial Sentiment Analysis During Pandemics: A Comparative Study of the H1N1 and the COVID-19 periods. Cogn. Comput. 14(1), 372–387 (2022)
Vintsyuk, T.K.: Speech discrimination by dynamic programming. Cybernetics 4(1), 52–57 (1968)
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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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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