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Forecasting cryptocurrency markets using recurrence and time-frequency analysis-based machine learning algorithms

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  • Kim, Dong Ha
  • Vanheusden, Frederique J.
  • Kim, Amee

Abstract

This study is the first to integrate recurrence plots, recurrence quantification analysis (RQA) and short-time Fourier Transform (STFT) to predict cryptocurrency market behaviour. Recurrence plots, RQA statistics and STFT spectrograms were calculated from return data and used as input in random forest algorithms as they are optimal tools for identifying non-linear dynamics in market data and analyse their frequency. Our optimised XGBoost algorithm provided a forecasting AUC above 76.7 % and accuracy of 70 % in predicting increasing or decreasing returns. This highlights the model’s ability to support cryptocurrency investment decision-making within an interpretable machine learning framework.

Suggested Citation

  • Kim, Dong Ha & Vanheusden, Frederique J. & Kim, Amee, 2025. "Forecasting cryptocurrency markets using recurrence and time-frequency analysis-based machine learning algorithms," Finance Research Letters, Elsevier, vol. 85(PE).
    • Handle: RePEc:eee:finlet:v:85:y:2025:i:pe:s1544612325015223
    DOI: 10.1016/j.frl.2025.108268
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    References listed on IDEAS

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    5. Fan Fang & Waichung Chung & Carmine Ventre & Michail Basios & Leslie Kanthan & Lingbo Li & Fan Wu, 2024. "Ascertaining price formation in cryptocurrency markets with machine learning," The European Journal of Finance, Taylor & Francis Journals, vol. 30(1), pages 78-100, January.
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