%0 Generic %1 https://doi.org/10.18419/darus-3455 %A Baier, Alexandra %A Frank, Daniel %D 2023 %I DaRUS %K myown simtech deeplearning pn4 from:alexbaier EXC2075 exc2075 %R 10.18419/DARUS-3455 %T deepsysid: System Identification Toolkit for Multistep Prediction using Deep Learning %U https://darus.uni-stuttgart.de/citation?persistentId=doi:10.18419/darus-3455 %X deepsysid is a system identification toolkit for multistep prediction using deep learning and hybrid methods. The toolkit is easy to use. After you follow the instructions in the README, you will be able to download a dataset, run hyperparameter optimization and identify your best-performing multistep prediction models with just three commands. The most current version of this software is available on GitHub.

@dataset{https://doi.org/10.18419/darus-3455, abstract = {deepsysid is a system identification toolkit for multistep prediction using deep learning and hybrid methods. The toolkit is easy to use. After you follow the instructions in the README, you will be able to download a dataset, run hyperparameter optimization and identify your best-performing multistep prediction models with just three commands. The most current version of this software is available on GitHub.}, added-at = {2023-05-22T13:38:52.000+0200}, author = {Baier, Alexandra and Frank, Daniel}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2f60f96ac732942a69308b7075a7c955c/analyticcomp}, doi = {10.18419/DARUS-3455}, interhash = {bdd0ba0fd06024e5606c28ad23efd3a9}, intrahash = {f60f96ac732942a69308b7075a7c955c}, keywords = {myown simtech deeplearning pn4 from:alexbaier EXC2075 exc2075}, publisher = {DaRUS}, timestamp = {2023-06-14T16:59:38.000+0200}, title = {deepsysid: System Identification Toolkit for Multistep Prediction using Deep Learning}, url = {https://darus.uni-stuttgart.de/citation?persistentId=doi:10.18419/darus-3455}, year = 2023 }