Tim Januschowski
Tim Januschowski
Databricks (prior: Zalando, AWS/Amazon, TU Berlin, University College Cork, Zuse Institute Berlin)
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Cited by
Cited by
DeepAR: Probabilistic forecasting with autoregressive recurrent networks
D Salinas, V Flunkert, J Gasthaus, T Januschowski
International journal of forecasting 36 (3), 1181-1191, 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
Forecasting: theory and practice
F Petropoulos, D Apiletti, V Assimakopoulos, MZ Babai, DK Barrow, ...
International Journal of Forecasting 38 (3), 705-871, 2022
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
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
Deep factors for forecasting
Y Wang, A Smola, D Maddix, J Gasthaus, D Foster, T Januschowski
International conference on machine learning, 6607-6617, 2019
On challenges in machine learning model management
S Schelter, F Biessmann, T Januschowski, D Salinas, S Seufert, ...
Criteria for classifying forecasting methods
T Januschowski, J Gasthaus, Y Wang, D Salinas, V Flunkert, ...
International Journal of Forecasting 36 (1), 167-177, 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
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
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
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
Forecasting with trees
T Januschowski, Y Wang, K Torkkola, T Erkkilä, H Hasson, J Gasthaus
International Journal of Forecasting 38 (4), 1473-1481, 2022
Neural temporal point processes: A review
O Shchur, AC Türkmen, T Januschowski, S Günnemann
arXiv preprint arXiv:2104.03528, 2021
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
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
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
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
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
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
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Articles 1–20