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• W81813850 abstract "Artificial neural networks have proved to be good at time-series forecasting problems, being widely studied at literature. Traditionally, shallow architectures were used due to convergence problems when dealing with deep models. Recent research findings enable deep architectures training, opening a new interesting research area called deep learning. This paper presents a study of deep learning techniques applied to time-series forecasting in a real indoor temperature forecasting task, studying performance due to different hyper-parameter configurations. When using deep models, better generalization performance at test set and an over-fitting reduction has been observed." @default.
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• W81813850 date "2013-01-01" @default.
• W81813850 modified "2023-10-17" @default.
• W81813850 title "Time-Series Forecasting of Indoor Temperature Using Pre-trained Deep Neural Networks" @default.
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• W81813850 doi "https://doi.org/10.1007/978-3-642-40728-4_57" @default.
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