| DeepAR: Probabilistic forecasting with autoregressive recurrent networks D Salinas, V Flunkert, J Gasthaus, T Januschowski International Journal of Forecasting 36 (3), 1181-1191, 2020 | 680 | 2020 |
| Deep state space models for time series forecasting SS Rangapuram, MW Seeger, J Gasthaus, L Stella, Y Wang, ... Advances in neural information processing systems 31, 2018 | 317 | 2018 |
| GluonTS: Probabilistic and Neural Time Series Modeling in Python. A Alexandrov, K Benidis, M Bohlke-Schneider, V Flunkert, J Gasthaus, ... J. Mach. Learn. Res. 21 (116), 1-6, 2020 | 141* | 2020 |
| On challenges in machine learning model management S Schelter, F Biessmann, T Januschowski, D Salinas, S Seufert, ... | 100 | 2018 |
| Deep factors for forecasting Y Wang, A Smola, D Maddix, J Gasthaus, D Foster, T Januschowski International conference on machine learning, 6607-6617, 2019 | 98 | 2019 |
| Criteria for classifying forecasting methods T Januschowski, J Gasthaus, Y Wang, D Salinas, V Flunkert, ... International Journal of Forecasting 36 (1), 167-177, 2020 | 96 | 2020 |
| Probabilistic demand forecasting at scale JH Böse, V Flunkert, J Gasthaus, T Januschowski, D Lange, D Salinas, ... Proceedings of the VLDB Endowment 10 (12), 1694-1705, 2017 | 88 | 2017 |
| Probabilistic forecasting with spline quantile function RNNs J Gasthaus, K Benidis, Y Wang, SS Rangapuram, D Salinas, V Flunkert, ... The 22nd international conference on artificial intelligence and statistics …, 2019 | 86 | 2019 |
| Deep Learning for Time Series Forecasting: Tutorial and Literature Survey K Benidis, SS Rangapuram, V Flunkert, Y Wang, D Maddix, C Turkmen, ... ACM Computing Surveys (CSUR), 2018 | 85* | 2018 |
| Forecasting: theory and practice F Petropoulos, D Apiletti, V Assimakopoulos, MZ Babai, DK Barrow, ... International Journal of Forecasting, 2022 | 76 | 2022 |
| Elastic machine learning algorithms in amazon sagemaker E Liberty, Z Karnin, B Xiang, L Rouesnel, B Coskun, R Nallapati, ... Proceedings of the 2020 ACM SIGMOD International Conference on Management of …, 2020 | 58 | 2020 |
| Rtp: Robust tenant placement for elastic in-memory database clusters J Schaffner, T Januschowski, M Kercher, T Kraska, H Plattner, MJ Franklin, ... Proceedings of the 2013 ACM SIGMOD International Conference on Management of …, 2013 | 54 | 2013 |
| Forecasting big time series: old and new C Faloutsos, J Gasthaus, T Januschowski, Y Wang Proceedings of the VLDB Endowment 11 (12), 2102-2105, 2018 | 51 | 2018 |
| Normalizing kalman filters for multivariate time series analysis E de Bézenac, SS Rangapuram, K Benidis, M Bohlke-Schneider, R Kurle, ... Advances in Neural Information Processing Systems 33, 2995-3007, 2020 | 29 | 2020 |
| Forecasting big time series: Theory and practice C Faloutsos, V Flunkert, J Gasthaus, T Januschowski, Y Wang Proceedings of the 25th ACM SIGKDD International Conference on Knowledge …, 2019 | 25 | 2019 |
| Robust tenant placement and migration in database-as-a-service environments H Plattner, J Schaffner, T Januschowski US Patent 9,525,731, 2016 | 19 | 2016 |
| Neural temporal point processes: A review O Shchur, AC Türkmen, T Januschowski, S Günnemann arXiv preprint arXiv:2104.03528, 2021 | 17 | 2021 |
| Deep learning for forecasting T Januschowski, J Gasthaus, Y Wang, SS Rangapuram, L Callot Foresight: The International Journal of Applied Forecasting, 35-41, 2018 | 17* | 2018 |
| Approximate bayesian inference in linear state space models for intermittent demand forecasting at scale M Seeger, S Rangapuram, Y Wang, D Salinas, J Gasthaus, ... arXiv preprint arXiv:1709.07638, 2017 | 17 | 2017 |
| Realistic tenant traces for enterprise DBaaS J Schaffner, T Januschowski 2013 IEEE 29th International Conference on Data Engineering Workshops (ICDEW …, 2013 | 17 | 2013 |
Jan GasthausAWS AI Labs, Amazon Web ServicesVerified email at ucl.ac.uk
