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Explainable Artificial Intelligence methods for financial time series

Author

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  • Giudici, Paolo
  • Piergallini, Alessandro
  • Recchioni, Maria Cristina
  • Raffinetti, Emanuela

Abstract

We consider the problem of developing explainable Artificial Intelligence methods to interpret the results of Artificial Intelligence models for time series data, taking time dependency into account. To this end, we extend the Shapley–Lorenz method, normalised by construction, to Artificial Intelligence for time series, such as neural networks and recurrent neural networks. We illustrate the application of our proposal to a time series of Bitcoin prices, which acts as the response variable, along with time series of classical financial prices, which act as explanatory variables.

Suggested Citation

  • Giudici, Paolo & Piergallini, Alessandro & Recchioni, Maria Cristina & Raffinetti, Emanuela, 2024. "Explainable Artificial Intelligence methods for financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 655(C).
    • Handle: RePEc:eee:phsmap:v:655:y:2024:i:c:s037843712400685x
    DOI: 10.1016/j.physa.2024.130176
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    References listed on IDEAS

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    1. Oglend, Atle & Kleppe, Tore Selland, 2017. "On the behavior of commodity prices when speculative storage is bounded," Journal of Economic Dynamics and Control, Elsevier, vol. 75(C), pages 52-69.
    2. Longbing Cao, 2021. "AI in Finance: Challenges, Techniques and Opportunities," Papers 2107.09051, arXiv.org.
    3. Giudici, Paolo & Raffinetti, Emanuela, 2023. "SAFE Artificial Intelligence in finance," Finance Research Letters, Elsevier, vol. 56(C).
    4. Paolo Giudici & Emanuela Raffinetti, 2020. "Lorenz Model Selection," Journal of Classification, Springer;The Classification Society, vol. 37(3), pages 754-768, October.
    5. Bracke, Philippe & Datta, Anupam & Jung, Carsten & Sen, Shayak, 2019. "Machine learning explainability in finance: an application to default risk analysis," Bank of England working papers 816, Bank of England.
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