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October 13, 2025Pakistan journal of commerce and social sciencesOpen Access

Unsupervised Machine Learning Based Anomaly Detection in High Frequency Data: Evidence from Cryptocurrency Market

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Authors

MLMuhammad Nouman LatifMKMuhittin KaplanAKAsad Ul Islam Khan

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Overview

Observational analysis assesses anomaly detection accuracy in cryptocurrencies, highlighting the efficiency of DBSCAN over three temporal resolutions.

Key Points

  • DBSCAN achieved the highest precision at 79.7%, offering effective anomaly detection compared to others.
  • The study compared multiple unsupervised learning models, demonstrating that DBSCAN outperforms OC-SVM and Isolation Forest in precision.
  • Using data from six major cryptocurrencies at different temporal resolutions, results confirmed consistent anomaly detection across methods.
  • The choice of algorithm affects anomaly detection, suggesting tailored approaches for financial monitoring in volatile cryptocurrency markets.

Cite This Study

Latif et al. (2025) studied this question.

synapsesocial.com/papers/68ece2abd1bb2827d1297489https://doi.org/10.64534/commer.2025.511
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