| DeepAR: Probabilistic forecasting with autoregressive recurrent networks D Salinas, V Flunkert, J Gasthaus, T Januschowski International Journal of Forecasting 36 (3), 1181-1191, 2020 | 1176 | 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 | 516 | 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 | 145 | 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 | 139 | 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 | 133 | 2009 |
| Gluonts: Probabilistic and neural time series modeling in python A Alexandrov, K Benidis, M Bohlke-Schneider, V Flunkert, J Gasthaus, ... The Journal of Machine Learning Research 21 (1), 4629-4634, 2020 | 132 | 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 | 129 | 2019 |
| 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 | 122 | 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 | 119 | 2017 |
| 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 | 108 | 2020 |
| 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 | 95 | 2020 |
| The sequence memoizer F Wood, J Gasthaus, C Archambeau, L James, YW Teh Communications of the ACM 54 (2), 91-98, 2011 | 87 | 2011 |
| Gluonts: Probabilistic time series models in python A Alexandrov, K Benidis, M Bohlke-Schneider, V Flunkert, J Gasthaus, ... arXiv preprint arXiv:1906.05264, 2019 | 75 | 2019 |
| 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 | 61 | 2018 |
| Dependent Dirichlet process spike sorting J Gasthaus, F Wood, D Gorur, Y Teh Advances in neural information processing systems 21, 2008 | 51 | 2008 |
| 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 | 41 | 2021 |
| Lossless compression based on the Sequence Memoizer J Gasthaus, F Wood, YW Teh 2010 data compression conference, 337-345, 2010 | 39 | 2010 |
| Forecasting with trees T Januschowski, Y Wang, K Torkkola, T Erkkilä, H Hasson, J Gasthaus International Journal of Forecasting 38 (4), 1473-1481, 2022 | 36 | 2022 |
| Deep rao-blackwellised particle filters for time series forecasting R Kurle, SS Rangapuram, E de Bézenac, S Günnemann, J Gasthaus Advances in Neural Information Processing Systems 33, 15371-15382, 2020 | 31 | 2020 |
| Improvements to the sequence memoizer J Gasthaus, Y Teh Advances in Neural Information Processing Systems 23, 2010 | 30 | 2010 |
Tim JanuschowskiZalando (formerly: AWS, Amazon, TU Berlin, University College Cork, Zuse Institute Berlin)Verified email at amazon.com
