Troncoso García, Ángela del RobledoMartínez Ballesteros, María del MarMartínez Álvarez, FranciscoTroncoso Lora, Alicia2024-04-122024-04-122023-06Troncoso García, Á.d.R., Martínez Ballesteros, M.d.M., Martínez Álvarez, F. y Troncoso Lora, A. (2023). Evolutionary computation to explain deep learning models for time series forecasting. En Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing (SAC '23) (433-436), Tallinn (Estonia): Association for Computing Machinery.https://hdl.handle.net/11441/156829Deep learning has become one of the most useful tools in the last years to mine information from large datasets. Despite the successful application to many research fields, deep learning is known as a black box approach and most experts experience difficulties to explain and interpret deep learning results. In this context, explainable artificial intelligence (XAI) is emerging with the aim of providing black box models with sufficient interpretability so that models can be easily understood by humans. The use of an evolutionary-based association rules extraction algorithm to explain deep learning models for multi-step time series forecasting is addressed in this work. This evolutionary application is proposed to be used with the predictions obtained by long-short term memory (LSTM) deep learning network. Data from Spanish electricity energy consumption has been used to assess the suitability of theapplication/pdf4engAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/openAccesshttps://doi.org/10.1145/3555776.3578994