| DeepAR: Probabilistic forecasting with autoregressive recurrent networks D Salinas, V Flunkert, J Gasthaus, T Januschowski International journal of forecasting 36 (3), 1181-1191, 2020 | 2485 | 2020 |
| Forecasting: theory and practice F Petropoulos, D Apiletti, V Assimakopoulos, MZ Babai, DK Barrow, ... International Journal of Forecasting 38 (3), 705-871, 2022 | 898 | 2022 |
| 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 | 893 | 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 | 399* | 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 | 347* | 2022 |
| Criteria for classifying forecasting methods T Januschowski, J Gasthaus, Y Wang, D Salinas, V Flunkert, ... International Journal of Forecasting 36 (1), 167-177, 2020 | 250 | 2020 |
| Deep factors for forecasting Y Wang, A Smola, D Maddix, J Gasthaus, D Foster, T Januschowski International conference on machine learning, 6607-6617, 2019 | 238 | 2019 |
| On challenges in machine learning model management S Schelter, F Biessmann, T Januschowski, D Salinas, S Seufert, ... | 233 | 2015 |
| 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 | 196 | 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 | 178 | 2017 |
| 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 | 147 | 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 | 132 | 2020 |
| Neural temporal point processes: A review O Shchur, AC Türkmen, T Januschowski, S Günnemann arXiv preprint arXiv:2104.03528, 2021 | 115 | 2021 |
| Forecasting with trees T Januschowski, Y Wang, K Torkkola, T Erkkilä, H Hasson, J Gasthaus International Journal of Forecasting 38 (4), 1473-1481, 2022 | 111 | 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 | 89 | 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 | 87 | 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 | 77 | 2021 |
| 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 | 59 | 2013 |
| Psa-gan: Progressive self attention gans for synthetic time series J Paul, BS Michael, M Pedro, K Shubham, SN Rajbir, F Valentin, G Jan, ... arXiv preprint arXiv:2108.00981, 2021 | 57* | 2021 |
| Forecasting intermittent and sparse time series: A unified probabilistic framework via deep renewal processes AC Türkmen, T Januschowski, Y Wang, AT Cemgil PLoS One 16 (11), e0259764, 2021 | 51* | 2021 |
Jan GasthausAWS AI Labs, Amazon Web ServicesVerified email at ucl.ac.uk
