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. © 2013 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Romeu, P., Zamora-Martínez, F., Botella-Rocamora, P., & Pardo, J. (2013). Time-series forecasting of indoor temperature using pre-trained deep neural networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8131 LNCS, pp. 451–458). https://doi.org/10.1007/978-3-642-40728-4_57
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