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Ponencia
Evaluation of the transformer architecture for univariate time series forecasting
(Springer, 2021)
The attention-based Transformer architecture is earning in- creasing popularity for many machine learning tasks. In this study, we aim to explore the suitability of Transformers for time series forecasting, which is a ...
Ponencia
Explaining Learned Patterns in Deep Learning by Association Rules Mining
(SpringerLink, 2023-08)
This paper proposes a novel approach that combines an association rule algorithm with a deep learning model to enhance the interpretability of prediction outcomes. The study aims to gain insights into the patterns that ...
Artículo
Data streams classification using deep learning under different speeds and drifts
(Oxford University Press, 2022)
Processing data streams arriving at high speed requires the development of models that can provide fast and accurate predictions. Although deep neural networks are the state-of-the-art for many machine learning tasks, ...
Artículo
Temporal convolutional networks applied to energy-related time series forecasting
(MDPI, 2020)
Modern energy systems collect high volumes of data that can provide valuable information about energy consumption. Electric companies can now use historical data to make informed decisions on energy production by forecasting ...