| DeepAR: Probabilistic forecasting with autoregressive recurrent networks D Salinas, V Flunkert, J Gasthaus, T Januschowski International journal of forecasting 36 (3), 1181-1191, 2020 | 1999 | 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 | 761 | 2018 |
| Gluonts: Probabilistic and neural time series modeling in python A Alexandrov, K Benidis, M Bohlke-Schneider, V Flunkert, J Gasthaus, ... Journal of Machine Learning Research 21 (116), 1-6, 2020 | 227 | 2020 |
| High-dimensional multivariate forecasting with low-rank gaussian copula processes D Salinas, M Bohlke-Schneider, L Callot, R Medico, J Gasthaus Advances in neural information processing systems 32, 2019 | 217 | 2019 |
| Deep factors for forecasting Y Wang, A Smola, D Maddix, J Gasthaus, D Foster, T Januschowski International conference on machine learning, 6607-6617, 2019 | 209 | 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 | 203 | 2020 |
| 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 | 172 | 2019 |
| 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 | 157 | 2017 |
| 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 55 (6), 1-36, 2022 | 140 | 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 | 134 | 2020 |
| A stochastic memoizer for sequence data F Wood, C Archambeau, J Gasthaus, L James, YW Teh Proceedings of the 26th annual international conference on machine learning …, 2009 | 134 | 2009 |
| Neural forecasting: Introduction and literature overview K Benidis, SS Rangapuram, V Flunkert, B Wang, D Maddix, C Turkmen, ... arXiv preprint arXiv:2004.10240 6, 2020 | 120 | 2020 |
| Gluonts: Probabilistic time series models in python A Alexandrov, K Benidis, M Bohlke-Schneider, V Flunkert, J Gasthaus, ... arXiv preprint arXiv:1906.05264, 2019 | 106 | 2019 |
| The sequence memoizer F Wood, J Gasthaus, C Archambeau, L James, YW Teh Communications of the ACM 54 (2), 91-98, 2011 | 90 | 2011 |
| Forecasting with trees T Januschowski, Y Wang, K Torkkola, T Erkkilä, H Hasson, J Gasthaus International Journal of Forecasting 38 (4), 1473-1481, 2022 | 85 | 2022 |
| 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 | 79 | 2018 |
| End-to-end learning of coherent probabilistic forecasts for hierarchical time series SS Rangapuram, LD Werner, K Benidis, P Mercado, J Gasthaus, ... International Conference on Machine Learning, 8832-8843, 2021 | 73 | 2021 |
| Neural contextual anomaly detection for time series CU Carmona, FX Aubet, V Flunkert, J Gasthaus arXiv preprint arXiv:2107.07702, 2021 | 66 | 2021 |
| Dependent Dirichlet process spike sorting J Gasthaus, F Wood, D Gorur, Y Teh Advances in neural information processing systems 21, 2008 | 51 | 2008 |
| Lossless compression based on the Sequence Memoizer J Gasthaus, F Wood, YW Teh 2010 data compression conference, 337-345, 2010 | 40 | 2010 |
Tim JanuschowskiDatabricks (prior: Zalando, AWS/Amazon, TU Berlin, University College Cork, Zuse Institute Berlin)在 databricks.com 的电子邮件经过验证
