| 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 | 739 | 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 | 335* | 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 | 249* | 2022 |
| Deep Factors for Forecasting Y Wang, A Smola, DC Maddix, J Gasthaus, D Foster, T Januschowski ICML 2019, arXiv preprint arXiv:1905.12417, 2019 | 207 | 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 | 200 | 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 | 171 | 2019 |
| Probabilistic Demand Forecasting at Scale YW Joos-Hendrik Boese, Valentin Flunkert, Jan Gasthaus, Tim Januschowski ... Proceedings of the VLDB Endowment 10 (12), 1694-1705, 2017 | 154* | 2017 |
| Elastic Machine Learning Algorithms in Amazon SageMaker E Liberty, Z Karnin, B Xiang, L Rouesnel, B Coskun, R Nallapati, ... SIGMOD, 2020 | 132 | 2020 |
| Earthformer: Exploring space-time transformers for earth system forecasting Z Gao, X Shi, H Wang, Y Zhu, YB Wang, M Li, DY Yeung Advances in Neural Information Processing Systems 35, 25390-25403, 2022 | 102 | 2022 |
| Forecasting with trees T Januschowski, Y Wang, K Torkkola, T Erkkilä, H Hasson, J Gasthaus International Journal of Forecasting 38 (4), 1473-1481, 2022 | 81 | 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 |
| Random Matrix Theory and Its Innovative Applications A Edelman, Y Wang Advances in Applied Mathematics, Modeling, and Computational Science, 91-116, 2012 | 77 | 2012 |
| Generalization Bounds for Online Learning Algorithms with Pairwise Loss Functions Y Wang, R Khardon, D Pechyony, R Jones Annual Conference on Learning Theory, 2012 | 65 | 2012 |
| Domain adaptation for time series forecasting via attention sharing X Jin, Y Park, D Maddix, H Wang, Y Wang International Conference on Machine Learning, 10280-10297, 2022 | 64 | 2022 |
| Zero-shot recommender systems H Ding, Y Ma, A Deoras, Y Wang, H Wang arXiv preprint arXiv:2105.08318, 2021 | 63 | 2021 |
| Bridging physics-based and data-driven modeling for learning dynamical systems R Wang, D Maddix, C Faloutsos, Y Wang, R Yu Learning for dynamics and control, 385-398, 2021 | 53 | 2021 |
| Nonparametric Bayesian estimation of periodic light curves Y Wang, R Khardon, P Protopapas The Astrophysical Journal 756 (1), 67, 2012 | 45 | 2012 |
| Deep factors with gaussian processes for forecasting DC Maddix, Y Wang, A Smola NeurIPS Workshop on Bayesian Deep Learning, 2018 | 43 | 2018 |
| 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 | 41* | 2021 |
| 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 | 38 | 2019 |
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
