{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T09:01:41Z","timestamp":1768208501242,"version":"3.49.0"},"reference-count":70,"publisher":"IEEE","license":[{"start":{"date-parts":[[2019,12,1]],"date-time":"2019-12-01T00:00:00Z","timestamp":1575158400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2019,12,1]],"date-time":"2019-12-01T00:00:00Z","timestamp":1575158400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2019,12,1]],"date-time":"2019-12-01T00:00:00Z","timestamp":1575158400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,12]]},"DOI":"10.1109\/bigdata47090.2019.9005496","type":"proceedings-article","created":{"date-parts":[[2020,2,25]],"date-time":"2020-02-25T06:05:34Z","timestamp":1582610734000},"page":"5679-5682","source":"Crossref","is-referenced-by-count":11,"title":["Benchmarking Deep Learning for Time Series: Challenges and Directions"],"prefix":"10.1109","author":[{"given":"Xinyuan","family":"Huang","sequence":"first","affiliation":[]},{"given":"Geoffrey C.","family":"Fox","sequence":"additional","affiliation":[]},{"given":"Sergey","family":"Serebryakov","sequence":"additional","affiliation":[]},{"given":"Ankur","family":"Mohan","sequence":"additional","affiliation":[]},{"given":"Pawel","family":"Morkisz","sequence":"additional","affiliation":[]},{"given":"Debojyoti","family":"Dutta","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2912896"},{"key":"ref38","doi-asserted-by":"crossref","first-page":"122","DOI":"10.3390\/info10040122","article-title":"A survey of deep learning methods for cyber security","volume":"10","author":"berman","year":"2019","journal-title":"Information"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1145\/2984511.2984565"},{"key":"ref32","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1145\/2897824.2925953","article-title":"Soli: Ubiquitous gesture sensing with millimeter wave radar","volume":"35","author":"lien","year":"2016","journal-title":"ACM Transactions on Graphics (TOG)"},{"key":"ref31","author":"lab","year":"0","journal-title":"CMU Graphics Lab Motion Capture Database"},{"key":"ref30","first-page":"783","article-title":"Hierarchical deep generative models for multi-rate multivariate time series","author":"che","year":"2018","journal-title":"International Conference on Machine Learning"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.2019.1800819"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1145\/2996758.2996764"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.5220\/0006639801080116"},{"key":"ref34","doi-asserted-by":"crossref","first-page":"1068","DOI":"10.3390\/rs11091068","article-title":"A survey of deep learning-based human activity recognition in radar","volume":"11","author":"li","year":"2019","journal-title":"Remote Sensing"},{"key":"ref60","article-title":"Deepar: Probabilistic forecasting with autoregressive recurrent networks","author":"salinas","year":"2019","journal-title":"International Journal of Forecasting"},{"key":"ref62","article-title":"Recurrent neural networks for time series forecasting: Current status and future directions","author":"hewamalage","year":"2019","journal-title":"arXiv preprint arXiv 1909 01771"},{"key":"ref61","article-title":"Fast es-rnn: A gpu implementation of the es-rnn algorithm","author":"redd","year":"2019","journal-title":"arXiv preprint arXiv 1907 09977"},{"key":"ref63","article-title":"Time series anomaly detection using convolutional neural networks and transfer learning","volume":"abs 1905 13628","author":"wen","year":"2019","journal-title":"CoRR"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/ab260c"},{"key":"ref64","doi-asserted-by":"crossref","DOI":"10.14569\/IJACSA.2017.080945","article-title":"Hybrid forecasting scheme for financial time-series data using neural network and statistical methods","volume":"8","author":"khairalla","year":"2017","journal-title":"International Journal of Advanced Computer Science and Applications"},{"key":"ref27","author":"roy","year":"2018","journal-title":"ChronoNet A deep recurrent neural network for abnormal EEG identification"},{"key":"ref65","article-title":"Graph convolutional recurrent neural network: Data-driven traffic forecasting","volume":"abs 1707 1926","author":"li","year":"2017","journal-title":"CoRR"},{"key":"ref66","author":"ng","year":"2019","journal-title":"Machine learning yearning technical strategy for AI engineers in the era of deep learning [M]"},{"key":"ref29","article-title":"Co-occurrence feature learning for skeleton based action recognition using regularized deep lstm networks","author":"zhu","year":"2016","journal-title":"THIRTIETH AAAI Conference on Artificial Intelligence"},{"key":"ref67","article-title":"Mlperf inference benchmark","author":"reddi","year":"2019","journal-title":"arXiv preprint arXiv 1911 12945"},{"key":"ref68","article-title":"Benchmarking tpu, gpu, and cpu platforms for deep learning","author":"wei","year":"2019","journal-title":"arXiv preprint arXiv 1907 11634"},{"key":"ref69","article-title":"Across-stack profiling and characterization of machine learning models on gpus","author":"li","year":"2019","journal-title":"arXiv preprint arXiv 1908 00310"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/IISWC.2018.8573476"},{"key":"ref1","author":"narang","year":"2017","journal-title":"Baidu DeepBench"},{"key":"ref20","year":"2019","journal-title":"EEG Motor Movement\/Imagery Dataset"},{"key":"ref22","year":"2019","journal-title":"EEG \/ ERP data available for free public download"},{"key":"ref21","year":"2019","journal-title":"Temple university hospital EEG (TUH EEG) resources"},{"key":"ref24","doi-asserted-by":"crossref","first-page":"115","DOI":"10.3390\/s16010115","article-title":"Deep convolutional and lstm recurrent neural networks for multimodal wearable activity recognition","volume":"16","author":"ord\u00f3\u00f1ez","year":"2016","journal-title":"SENSORS"},{"key":"ref23","year":"0","journal-title":"Mimic data"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-018-0268-3"},{"key":"ref25","article-title":"Learning representations from eeg with deep recurrent-convolutional neural networks","author":"bashivan","year":"2016","journal-title":"International Conference on Learning Representations"},{"key":"ref50","year":"2019","journal-title":"LANL earthquake prediction can you predict upcoming laboratory earthquakes?"},{"key":"ref51","year":"2019","journal-title":"Earthquake hazards program Search earthquake catalog"},{"key":"ref59","article-title":"Autoregressive convolutional neural networks for asynchronous time series","author":"binkowski","year":"2017","journal-title":"arXiv preprint arXiv 1703 04529"},{"key":"ref58","year":"0","journal-title":"Individual Household Electric Power Consumption Data Set"},{"key":"ref57","year":"0","journal-title":"M4 competition combined multi-area time series"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-92297-3_10"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-018-0438-y"},{"key":"ref54","author":"smart","year":"2019","journal-title":"Artificial intelligence takes on earthquake prediction"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-019-1116-4"},{"key":"ref52","article-title":"New models for integrated inquiry: Fusion energy exemplar","author":"tang","year":"2018","journal-title":"Online Resource for Big Data and Extreme-Scale Computing Workshop"},{"key":"ref10","year":"0","journal-title":"TLC Trip Record Data"},{"key":"ref11","year":"2019","journal-title":"Uber trip data from a freedom of information request to NYC’s taxi & limousine commission"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1002\/for.2543"},{"key":"ref12","year":"2019","journal-title":"Uber movement information"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/ICDMW.2017.19"},{"key":"ref14","first-page":"1","article-title":"Time-series extreme event forecasting with neural networks at uber","author":"laptev","year":"2017","journal-title":"International Conference on Machine Learning"},{"key":"ref15","article-title":"Diffusion convolutional recurrent neural network: Data-driven traffic forecasting","author":"li","year":"2018","journal-title":"International Conference on Learning Representations"},{"key":"ref16","article-title":"St-unet: A spatio-temporal unetwork for graph-structured time series modeling","author":"yu","year":"2019","journal-title":"arXiv preprint arXiv 1903 00066"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/INSS.2010.5573462"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2012.12.014"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2004.827072"},{"key":"ref4","article-title":"Mlperf training benchmark","author":"mattson","year":"2019","journal-title":"arXiv preprint arXiv 1910 01500"},{"key":"ref3","first-page":"102","article-title":"Dawnbench: An end-to-end deep learning benchmark and competition","volume":"100","author":"coleman","year":"2017","journal-title":"Training"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.is.2015.04.007"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1515\/9780691218632"},{"key":"ref8","article-title":"Deep learning for time-series analysis","author":"gamboa","year":"2017","journal-title":"arXiv preprint arXiv 1701 01887"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-019-00619-1"},{"key":"ref49","year":"0","journal-title":"United states historical climatology network (ushcn) daily dataset"},{"key":"ref9","year":"0","journal-title":"Traffic data collected from california department of transportation freeway detectors"},{"key":"ref46","author":"huch","year":"2019","journal-title":"Kaggle software anomaly competition"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.3390\/jrfm12010017"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/MALTESQUE.2018.8368453"},{"key":"ref47","article-title":"Multivariate industrial time series with cyber-attack simulation: Fault detection using an lstm-based predictive data model","author":"filonov","year":"2016","journal-title":"arXiv preprint arXiv 1612 06676"},{"key":"ref42","article-title":"Benchmarking deep sequential models on volatility predictions for financial time series","author":"zhang","year":"2018","journal-title":"arXiv preprint arXiv 1811 03711"},{"key":"ref41","year":"0","journal-title":"Elliptic data set"},{"key":"ref44","article-title":"Anti-money laundering in bitcoin: Experimenting with graph convolutional networks for financial forensics","author":"weber","year":"2019","journal-title":"Proceedings of the KDD Workshop on Anomaly Detection in Finance"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2019.2907260"}],"event":{"name":"2019 IEEE International Conference on Big Data (Big Data)","location":"Los Angeles, CA, USA","start":{"date-parts":[[2019,12,9]]},"end":{"date-parts":[[2019,12,12]]}},"container-title":["2019 IEEE International Conference on Big Data (Big Data)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/8986695\/9005444\/09005496.pdf?arnumber=9005496","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,17]],"date-time":"2022-07-17T21:47:20Z","timestamp":1658094440000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9005496\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12]]},"references-count":70,"URL":"https:\/\/doi.org\/10.1109\/bigdata47090.2019.9005496","relation":{},"subject":[],"published":{"date-parts":[[2019,12]]}}}