%0 Conference Paper %1 https://doi.org/10.48550/arxiv.2205.05511 %A Deng, Difan %A Karl, Florian %A Hutter, Frank %A Bischl, Bernd %A Lindauer, Marius %B Proceedings of the European Conference on Machine Learning (ECML) %D 2022 %I arXiv %K automl leibnizailab myown %R 10.48550/ARXIV.2205.05511 %T Efficient Automated Deep Learning for Time Series Forecasting %U https://arxiv.org/abs/2205.05511

@inproceedings{https://doi.org/10.48550/arxiv.2205.05511, added-at = {2022-11-17T12:14:02.000+0100}, author = {Deng, Difan and Karl, Florian and Hutter, Frank and Bischl, Bernd and Lindauer, Marius}, biburl = {https://www.bibsonomy.org/bibtex/2aaab70add0c0e4b1c4abe5d0220a643f/ail3s}, booktitle = {Proceedings of the European Conference on Machine Learning (ECML)}, copyright = {Creative Commons Attribution 4.0 International}, doi = {10.48550/ARXIV.2205.05511}, eventtitle = {ECML}, interhash = {a59c34efeed475019399071f48d71ffa}, intrahash = {aaab70add0c0e4b1c4abe5d0220a643f}, keywords = {automl leibnizailab myown}, publisher = {arXiv}, timestamp = {2022-11-18T12:51:54.000+0100}, title = {Efficient Automated Deep Learning for Time Series Forecasting}, url = {https://arxiv.org/abs/2205.05511}, year = 2022 }