@inproceedings{
 title = {Benchmarking Deep Learning for Time Series: Challenges and Directions},
 type = {inproceedings},
 year = {2019},
 keywords = {benchmark,deep learning,machine learning,performance,time series},
 pages = {5679-5682},
 month = {12},
 publisher = {Institute of Electrical and Electronics Engineers Inc.},
 day = {1},
 id = {4fe41ff0-884c-3804-bb94-5d55c61eba5b},
 created = {2020-04-21T18:42:11.807Z},
 accessed = {2020-04-21},
 file_attached = {false},
 profile_id = {42d295c0-0737-38d6-8b43-508cab6ea85d},
 group_id = {f0704cbc-a3d0-3264-b41e-15f4ed0c92ee},
 last_modified = {2020-04-21T18:42:11.905Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 abstract = {Deep learning for time series is an emerging area with close ties to industry, yet under represented in performance benchmarks for machine learning systems. In this paper, we present a landscape of deep learning applications applied to time series, and discuss the challenges and directions towards building a robust performance benchmark of deep learning workloads for time series data.},
 bibtype = {inproceedings},
 author = {Huang, Xinyuan and Fox, Geoffrey C. and Serebryakov, Sergey and Mohan, Ankur and Morkisz, Pawel and Dutta, Debojyoti},
 doi = {10.1109/BigData47090.2019.9005496},
 booktitle = {Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019}
}

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