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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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Real-Time Anomaly Prediction from Cryptocurrency Time Series

  • 会议论文

  • 发表日期：

    2025年

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  • Antonio Pellicani,
  • Gianvito Pio,
  • Sašo Džeroski,
  • Michelangelo Ceci

摘要

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.