Web: http://arxiv.org/abs/2205.05511

May 12, 2022, 1:11 a.m. | Difan Deng, Florian Karl, Frank Hutter, Bernd Bischl, Marius Lindauer

cs.LG updates on arXiv.org arxiv.org

Recent years have witnessed tremendously improved efficiency of Automated
Machine Learning (AutoML), especially Automated Deep Learning (AutoDL) systems,
but recent work focuses on tabular, image, or NLP tasks. So far, little
attention has been paid to general AutoDL frameworks for time series
forecasting, despite the enormous success in applying different novel
architectures to such tasks. In this paper, we propose an efficient approach
for the joint optimization of neural architecture and hyperparameters of the
entire data processing pipeline for time …

arxiv deep deep learning forecasting learning time time series time series forecasting

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