| 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 | 857 | 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 | 258 | 2020 |
| 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 | 195 | 2022 |
| 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 | 190 | 2019 |
| The total variation on hypergraphs-learning on hypergraphs revisited M Hein, S Setzer, L Jost, SS Rangapuram Advances in Neural Information Processing Systems 26, 2013 | 170 | 2013 |
| 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 | 145 | 2020 |
| 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 | 127 | 2020 |
| 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 | 124 | 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 | 111 | 2019 |
| Towards realistic team formation in social networks based on densest subgraphs SS Rangapuram, T Bühler, M Hein Proceedings of the 22nd international conference on World Wide Web, 1077-1088, 2013 | 111 | 2013 |
| Constrained 1-spectral clustering SS Rangapuram, M Hein Artificial Intelligence and Statistics, 1143-1151, 2012 | 107 | 2012 |
| Chronos: Learning the language of time series AF Ansari, L Stella, C Turkmen, X Zhang, P Mercado, H Shen, O Shchur, ... arXiv preprint arXiv:2403.07815, 2024 | 97 | 2024 |
| 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 | 78 | 2021 |
| Neural flows: Efficient alternative to neural ODEs M Biloš, J Sommer, SS Rangapuram, T Januschowski, S Günnemann Advances in neural information processing systems 34, 21325-21337, 2021 | 69 | 2021 |
| 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 | 36 | 2020 |
| 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 | 30 | 2017 |
| Multivariate time series forecasting with latent graph inference VG Satorras, SS Rangapuram, T Januschowski arXiv preprint arXiv:2203.03423, 2022 | 27 | 2022 |
| Tight continuous relaxation of the balanced k-cut problem SS Rangapuram, PK Mudrakarta, M Hein Advances in Neural Information Processing Systems 27, 2014 | 27 | 2014 |
| GluonTS: Probabilistic time series modeling in Python A Alexandrov, K Benidis, M Bohlke-Schneider, V Flunkert, J Gasthaus, ... arXiv preprint arXiv:1906.05264, 2019 | 25 | 2019 |
| Deep Learning for Forecasting: Current Trends and Challenges. T Januschowski, J Gasthaus, Y Wang, SS Rangapuram, L Callot Foresight: The International Journal of Applied Forecasting, 2018 | 22 | 2018 |
Tim JanuschowskiDatabricks (prior: Zalando, AWS/Amazon, TU Berlin, University College Cork, Zuse Institute Berlin)Bestätigte E-Mail-Adresse bei databricks.com
