| DeepAR: Probabilistic forecasting with autoregressive recurrent networks D Salinas, V Flunkert, J Gasthaus, T Januschowski International Journal of Forecasting 36 (3), 1181-1191, 2020 | 205 | 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 | 119 | 2009 |
| 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, 7785-7794, 2018 | 116 | 2018 |
| The sequence memoizer F Wood, J Gasthaus, C Archambeau, L James, YW Teh Communications of the ACM 54 (2), 91-98, 2011 | 73 | 2011 |
| Dependent Dirichlet process spike sorting J Gasthaus, F Wood, D Gorur, YW Teh Advances in neural information processing systems, 497-504, 2009 | 51 | 2009 |
| 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 | 50 | 2017 |
| Lossless compression based on the Sequence Memoizer J Gasthaus, F Wood, YW Teh 2010 data compression conference, 337-345, 2010 | 38 | 2010 |
| Criteria for classifying forecasting methods T Januschowski, J Gasthaus, Y Wang, D Salinas, V Flunkert, ... International Journal of Forecasting 36 (1), 167-177, 2020 | 30 | 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 | 30 | 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 | 29 | 2019 |
| Improvements to the sequence memoizer J Gasthaus, Y Teh Advances in Neural Information Processing Systems 23, 685-693, 2010 | 28 | 2010 |
| 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 | 27 | 2018 |
| Gluonts: Probabilistic time series models in python A Alexandrov, K Benidis, M Bohlke-Schneider, V Flunkert, J Gasthaus, ... arXiv preprint arXiv:1906.05264, 2019 | 25 | 2019 |
| High-dimensional multivariate forecasting with low-rank gaussian copula processes D Salinas, M Bohlke-Schneider, L Callot, R Medico, J Gasthaus arXiv preprint arXiv:1910.03002, 2019 | 17 | 2019 |
| Neural forecasting: Introduction and literature overview K Benidis, SS Rangapuram, V Flunkert, B Wang, D Maddix, C Turkmen, ... arXiv preprint arXiv:2004.10240, 2020 | 15 | 2020 |
| GP-select: Accelerating EM using adaptive subspace preselection JA Shelton, J Gasthaus, Z Dai, J Lücke, A Gretton Neural computation 29 (8), 2177-2202, 2017 | 15 | 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 | 14 | 2020 |
| FIASCO: Filtering the internet by automatic subtree classification, osnabruck D Bauer, J Degen, X Deng, P Herger, J Gasthaus, E Giesbrecht, L Jansen, ... Building and Exploring Web Corpora: Proceedings of the 3rd Web as Corpus …, 2007 | 14 | 2007 |
| Spike sorting using time-varying DIRICHLET process mixture models JA Gasthaus, F Wood PhD thesis. University College London, London, 2008 | 10 | 2008 |
| Now available in amazon sagemaker: Deepar algorithm for more accurate time series forecasting T Januschowski, D Arpin, D Salinas, V Flunkert, J Gasthaus, L Stella, ... | 9 | 2018 |
Tim JanuschowskiAmazonVerified email at amazon.com
