Explainable Artificial Intelligence methods for financial time series

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

Physica A: Statistical Mechanics and its Applications, 2024, vol. 655, issue C

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.

Keywords: Explainable Artificial Intelligence; LSTM and GRU; Shapley–Lorenz values; Financial time series; Bitcoin prices (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:655:y:2024:i:c:s037843712400685x

DOI: 10.1016/j.physa.2024.130176

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