Tim Januschowski

64 papers A* 13A 3B 1Journal 37Unranked 10

Year	Rank	Type	Title / Venue / Authors
2025	—	`conf`	Unity Catalog: Open and Universal Governance for the Lakehouse and Beyond. SIGMOD Conference Companion Ramesh Chandra, Haogang Chen, Ray Matharu, Sarah Cai, Jeff Chen, Priyam Dutta, Bogdan Ghita, Todd Greenstein, Gopal Holla, Peng Huang, Yuchen Huo, Adrian Ionescu, Adriana Ispas, Tim Januschowski, Vihang Karajgaonkar, Stefania Leone, David Lewis, Andrew Li, Nong Li, Cheng Lian, Stephen Link, Qing Lu, Yesheng Ma, Chris Pettitt, Vijayan Prabhakaran, Bogdan Raducanu, Kyle Rong, Paul Roome, Samarth Shetty, Sean Smith, Xiaotong Sun, Yuyuan Tang, Weitao Wen, Lei Xia, Junlin Zeng, Ben Zhang, Reynold Xin, Matei Zaharia
2024	J	`jnl`	A Flexible Forecasting Stack. Proc. VLDB Endow. Tim Januschowski, Yuyang Wang, Jan Gasthaus, Syama Sundar Rangapuram, Caner Türkmen, Jasper Zschiegner, Lorenzo Stella, Michael Bohlke-Schneider, Danielle C. Maddix, Konstantinos Benidis, Alexander Alexandrov, Christos Faloutsos, Sebastian Schelter
2023	J	`jnl`	Causal Forecasting for Pricing. CoRR Douglas Schultz, Johannes Stephan, Julian Sieber, Trudie Yeh, Manuel Kunz, Patrick Doupe, Tim Januschowski
2023	A	`conf`	Coherent Probabilistic Forecasting of Temporal Hierarchies. AISTATS Syama Sundar Rangapuram, Shubham Kapoor, Rajbir-Singh Nirwan, Pedro Mercado, Tim Januschowski, Yuyang Wang, Michael Bohlke-Schneider
2023	J	`jnl`	Deep Learning based Forecasting: a case study from the online fashion industry. CoRR Manuel Kunz, Stefan Birr, Mones Raslan, Lei Ma, Zhen Li, Adèle Gouttes, Mateusz Koren, Tofigh Naghibi, Johannes Stephan, Mariia Bulycheva, Matthias Grzeschik, Armin Kekic, Michael Narodovitch, Kashif Rasul, Julian Sieber, Tim Januschowski
2023	J	`jnl`	Deep Learning for Time Series Forecasting: Tutorial and Literature Survey. ACM Comput. Surv. Konstantinos Benidis, Syama Sundar Rangapuram, Valentin Flunkert, Yuyang Wang, Danielle C. Maddix, Ali Caner Türkmen, Jan Gasthaus, Michael Bohlke-Schneider, David Salinas, Lorenzo Stella, François-Xavier Aubet, Laurent Callot, Tim Januschowski
2023	J	`jnl`	Deep Non-Parametric Time Series Forecaster. CoRR Syama Sundar Rangapuram, Jan Gasthaus, Lorenzo Stella, Valentin Flunkert, David Salinas, Yuyang Wang, Tim Januschowski
2022	J	`jnl`	Criteria for Classifying Forecasting Methods. CoRR Tim Januschowski, Jan Gasthaus, Yuyang Wang, David Salinas, Valentin Flunkert, Michael Bohlke-Schneider, Laurent Callot
2022	J	`jnl`	Diverse Counterfactual Explanations for Anomaly Detection in Time Series. CoRR Déborah Sulem, Michele Donini, Muhammad Bilal Zafar, Francois-Xavier Aubet, Jan Gasthaus, Tim Januschowski, Sanjiv Das, Krishnaram Kenthapadi, Cédric Archambeau
2022	J	`jnl`	Intrinsic Anomaly Detection for Multi-Variate Time Series. CoRR Stephan Rabanser, Tim Januschowski, Kashif Rasul, Oliver Borchert, Richard Kurle, Jan Gasthaus, Michael Bohlke-Schneider, Nicolas Papernot, Valentin Flunkert
2022	J	`jnl`	Multi-Objective Model Selection for Time Series Forecasting. CoRR Oliver Borchert, David Salinas, Valentin Flunkert, Tim Januschowski, Stephan Günnemann
2022	A	`conf`	Multivariate Quantile Function Forecaster. AISTATS Kelvin Kan, François-Xavier Aubet, Tim Januschowski, Youngsuk Park, Konstantinos Benidis, Lars Ruthotto, Jan Gasthaus
2022	J	`jnl`	Multivariate Quantile Function Forecaster. CoRR Kelvin Kan, François-Xavier Aubet, Tim Januschowski, Youngsuk Park, Konstantinos Benidis, Lars Ruthotto, Jan Gasthaus
2022	J	`jnl`	Multivariate Time Series Forecasting with Latent Graph Inference. CoRR Victor Garcia Satorras, Syama Sundar Rangapuram, Tim Januschowski
2022	A*	`conf`	On the detrimental effect of invariances in the likelihood for variational inference. NeurIPS Richard Kurle, Ralf Herbrich, Tim Januschowski, Yuyang Wang, Jan Gasthaus
2022	J	`jnl`	On the detrimental effect of invariances in the likelihood for variational inference. CoRR Richard Kurle, Ralf Herbrich, Tim Januschowski, Yuyang Wang, Jan Gasthaus
2022	A*	`conf`	PSA-GAN: Progressive Self Attention GANs for Synthetic Time Series. ICLR Paul Jeha, Michael Bohlke-Schneider, Pedro Mercado, Shubham Kapoor, Rajbir-Singh Nirwan, Valentin Flunkert, Jan Gasthaus, Tim Januschowski
2022	J	`jnl`	Resilient Neural Forecasting Systems. CoRR Michael Bohlke-Schneider, Shubham Kapoor, Tim Januschowski
2021	J	`jnl`	A Study of Joint Graph Inference and Forecasting. CoRR Daniel Zügner, François-Xavier Aubet, Victor Garcia Satorras, Tim Januschowski, Stephan Günnemann, Jan Gasthaus
2021	A*	`conf`	Deep Explicit Duration Switching Models for Time Series. NeurIPS Abdul Fatir Ansari, Konstantinos Benidis, Richard Kurle, Ali Caner Türkmen, Harold Soh, Alexander J. Smola, Bernie Wang, Tim Januschowski
2021	J	`jnl`	Deep Explicit Duration Switching Models for Time Series. CoRR Abdul Fatir Ansari, Konstantinos Benidis, Richard Kurle, Ali Caner Türkmen, Harold Soh, Alexander J. Smola, Yuyang Wang, Tim Januschowski
2021	A*	`conf`	Detecting Anomalous Event Sequences with Temporal Point Processes. NeurIPS Oleksandr Shchur, Ali Caner Türkmen, Tim Januschowski, Jan Gasthaus, Stephan Günnemann
2021	J	`jnl`	Detecting Anomalous Event Sequences with Temporal Point Processes. CoRR Oleksandr Shchur, Ali Caner Türkmen, Tim Januschowski, Jan Gasthaus, Stephan Günnemann
2021	A*	`conf`	End-to-End Learning of Coherent Probabilistic Forecasts for Hierarchical Time Series. ICML Syama Sundar Rangapuram, Lucien D. Werner, Konstantinos Benidis, Pedro Mercado, Jan Gasthaus, Tim Januschowski
2021	J	`jnl`	Meta-Forecasting by combining Global Deep Representations with Local Adaptation. CoRR Riccardo Grazzi, Valentin Flunkert, David Salinas, Tim Januschowski, Matthias W. Seeger, Cédric Archambeau
2021	A*	`conf`	Neural Flows: Efficient Alternative to Neural ODEs. NeurIPS Marin Bilos, Johanna Sommer, Syama Sundar Rangapuram, Tim Januschowski, Stephan Günnemann
2021	J	`jnl`	Neural Flows: Efficient Alternative to Neural ODEs. CoRR Marin Bilos, Johanna Sommer, Syama Sundar Rangapuram, Tim Januschowski, Stephan Günnemann
2021	A*	`conf`	Neural Temporal Point Processes: A Review. IJCAI Oleksandr Shchur, Ali Caner Türkmen, Tim Januschowski, Stephan Günnemann
2021	J	`jnl`	Neural Temporal Point Processes: A Review. CoRR Oleksandr Shchur, Ali Caner Türkmen, Tim Januschowski, Stephan Günnemann
2021	A*	`conf`	Online false discovery rate control for anomaly detection in time series. NeurIPS Quentin Rebjock, Baris Kurt, Tim Januschowski, Laurent Callot
2021	J	`jnl`	Online false discovery rate control for anomaly detection in time series. CoRR Quentin Rebjock, Baris Kurt, Tim Januschowski, Laurent Callot
2021	J	`jnl`	PSA-GAN: Progressive Self Attention GANs for Synthetic Time Series. CoRR Paul Jeha, Michael Bohlke-Schneider, Pedro Mercado, Rajbir-Singh Nirwan, Shubham Kapoor, Valentin Flunkert, Jan Gasthaus, Tim Januschowski
2021	A*	`conf`	Probabilistic Forecasting: A Level-Set Approach. NeurIPS Hilaf Hasson, Bernie Wang, Tim Januschowski, Jan Gasthaus
2020	—	`conf`	A Simple and Effective Predictive Resource Scaling Heuristic for Large-scale Cloud Applications. AIDB@VLDB Quentin Rebjock, Valentin Flunkert, Tim Januschowski, Laurent Callot, Joel Castellon
2020	J	`jnl`	A simple and effective predictive resource scaling heuristic for large-scale cloud applications. CoRR Valentin Flunkert, Quentin Rebjock, Joel Castellon, Laurent Callot, Tim Januschowski
2020	—	`conf`	Anomaly Detection at Scale: The Case for Deep Distributional Time Series Models. ICSOC Workshops Fadhel Ayed, Lorenzo Stella, Tim Januschowski, Jan Gasthaus
2020	J	`jnl`	Anomaly Detection at Scale: The Case for Deep Distributional Time Series Models. CoRR Fadhel Ayed, Lorenzo Stella, Tim Januschowski, Jan Gasthaus
2020	—	`conf`	Elastic Machine Learning Algorithms in Amazon SageMaker. SIGMOD Conference Edo Liberty, Zohar S. Karnin, Bing Xiang, Laurence Rouesnel, Baris Coskun, Ramesh Nallapati, Julio Delgado, Amir Sadoughi, Yury Astashonok, Piali Das, Can Balioglu, Saswata Chakravarty, Madhav Jha, Philip Gautier, David Arpin, Tim Januschowski, Valentin Flunkert, Yuyang Wang, Jan Gasthaus, Lorenzo Stella, Syama Sundar Rangapuram, David Salinas, Sebastian Schelter, Alex Smola
2020	—	`conf`	Forecasting Big Time Series: Theory and Practice. WWW (Companion Volume) Christos Faloutsos, Valentin Flunkert, Jan Gasthaus, Tim Januschowski, Yuyang Wang
2020	J	`jnl`	Forecasting: theory and practice. CoRR Fotios Petropoulos, Daniele Apiletti, Vassilios Assimakopoulos, Mohamed Zied Babai, Devon K. Barrow, Souhaib Ben Taieb, Christoph Bergmeir, Ricardo J. Bessa, Jakub Bijak, John E. Boylan, Jethro Browell, Claudio Carnevale, Jennifer L. Castle, Pasquale Cirillo, Michael P. Clements, Clara Cordeiro, Fernando Luiz Cyrino Oliveira, Shari De Baets, Alexander Dokumentov, Joanne Ellison, Piotr Fiszeder, Philip Hans Franses, David T. Frazier, Michael Gilliland, Mustafa Sinan Gönül, Paul Goodwin, Luigi Grossi, Yael Grushka-Cockayne, Mariangela Guidolin, Massimo Guidolin, Ulrich Gunter, Xiaojia Guo, Renato Guseo, Nigel Harvey, David F. Hendry, Ross Hollyman, Tim Januschowski, Jooyoung Jeon, Victor Richmond R. Jose, Yanfei Kang, Anne B. Koehler, Stephan Kolassa, Nikolaos Kourentzes, Sonia Leva, Feng Li
2020	J	`jnl`	GluonTS: Probabilistic and Neural Time Series Modeling in Python. J. Mach. Learn. Res. Alexander Alexandrov, Konstantinos Benidis, Michael Bohlke-Schneider, Valentin Flunkert, Jan Gasthaus, Tim Januschowski, Danielle C. Maddix, Syama Sundar Rangapuram, David Salinas, Jasper Schulz, Lorenzo Stella, Ali Caner Türkmen, Yuyang Wang
2020	J	`jnl`	Intermittent Demand Forecasting with Renewal Processes. CoRR Ali Caner Türkmen, Tim Januschowski, Yuyang Wang, Ali Taylan Cemgil
2020	J	`jnl`	Neural forecasting: Introduction and literature overview. CoRR Konstantinos Benidis, Syama Sundar Rangapuram, Valentin Flunkert, Bernie Wang, Danielle C. Maddix, Ali Caner Türkmen, Jan Gasthaus, Michael Bohlke-Schneider, David Salinas, Lorenzo Stella, Laurent Callot, Tim Januschowski
2020	A*	`conf`	Normalizing Kalman Filters for Multivariate Time Series Analysis. NeurIPS Emmanuel de Bézenac, Syama Sundar Rangapuram, Konstantinos Benidis, Michael Bohlke-Schneider, Richard Kurle, Lorenzo Stella, Hilaf Hasson, Patrick Gallinari, Tim Januschowski
2020	—	`conf`	Resilient Neural Forecasting Systems. DEEM@SIGMOD Michael Bohlke-Schneider, Shubham Kapoor, Tim Januschowski
2020	J	`jnl`	The Effectiveness of Discretization in Forecasting: An Empirical Study on Neural Time Series Models. CoRR Stephan Rabanser, Tim Januschowski, Valentin Flunkert, David Salinas, Jan Gasthaus
2019	—	`conf`	Classical and Contemporary Approaches to Big Time Series Forecasting. SIGMOD Conference Christos Faloutsos, Jan Gasthaus, Tim Januschowski, Yuyang Wang
2019	A*	`conf`	Deep Factors for Forecasting. ICML Yuyang Wang, Alex Smola, Danielle C. Maddix, Jan Gasthaus, Dean P. Foster, Tim Januschowski
2019	J	`jnl`	Deep Factors for Forecasting. CoRR Yuyang Wang, Alex Smola, Danielle C. Maddix, Jan Gasthaus, Dean P. Foster, Tim Januschowski
2019	—	`conf`	End-to-End Benchmarking of Deep Learning Platforms. TPCTC Vincent Deuschle, Alexander Alexandrov, Tim Januschowski, Volker Markl
2019	A*	`conf`	Forecasting Big Time Series: Theory and Practice. KDD Christos Faloutsos, Valentin Flunkert, Jan Gasthaus, Tim Januschowski, Yuyang Wang
2019	J	`jnl`	GluonTS: Probabilistic Time Series Models in Python. CoRR Alexander Alexandrov, Konstantinos Benidis, Michael Bohlke-Schneider, Valentin Flunkert, Jan Gasthaus, Tim Januschowski, Danielle C. Maddix, Syama Sundar Rangapuram, David Salinas, Jasper Schulz, Lorenzo Stella, Ali Caner Türkmen, Yuyang Wang
2019	J	`jnl`	Intermittent Demand Forecasting with Deep Renewal Processes. CoRR Ali Caner Türkmen, Yuyang Wang, Tim Januschowski
2019	A	`conf`	Probabilistic Forecasting with Spline Quantile Function RNNs. AISTATS Jan Gasthaus, Konstantinos Benidis, Yuyang Wang, Syama Sundar Rangapuram, David Salinas, Valentin Flunkert, Tim Januschowski
2018	A*	`conf`	Deep State Space Models for Time Series Forecasting. NeurIPS Syama Sundar Rangapuram, Matthias W. Seeger, Jan Gasthaus, Lorenzo Stella, Yuyang Wang, Tim Januschowski
2018	J	`jnl`	Forecasting Big Time Series: Old and New. Proc. VLDB Endow. Christos Faloutsos, Jan Gasthaus, Tim Januschowski, Yuyang Wang
2018	J	`jnl`	On Challenges in Machine Learning Model Management. IEEE Data Eng. Bull. Sebastian Schelter, Felix Bießmann, Tim Januschowski, David Salinas, Stephan Seufert, Gyuri Szarvas
2017	J	`jnl`	Approximate Bayesian Inference in Linear State Space Models for Intermittent Demand Forecasting at Scale. CoRR Matthias W. Seeger, Syama Sundar Rangapuram, Yuyang Wang, David Salinas, Jan Gasthaus, Tim Januschowski, Valentin Flunkert
2017	J	`jnl`	Probabilistic Demand Forecasting at Scale. Proc. VLDB Endow. Joos-Hendrik Boese, Valentin Flunkert, Jan Gasthaus, Tim Januschowski, Dustin Lange, David Salinas, Sebastian Schelter, Matthias W. Seeger, Bernie Wang
2013	—	`conf`	RTP: robust tenant placement for elastic in-memory database clusters. SIGMOD Conference Jan Schaffner, Tim Januschowski, Megan Kercher, Tim Kraska, Hasso Plattner, Michael J. Franklin, Dean Jacobs
2013	—	`conf`	Realistic tenant traces for enterprise DBaaS. ICDE Workshops Jan Schaffner, Tim Januschowski
2011	B	`conf`	Branch-Cut-and-Propagate for the Maximum CPAIOR Tim Januschowski, Marc E. Pfetsch
2011	J	`jnl`	The maximum k-colorable subgraph problem and orbitopes. Discret. Optim. Tim Januschowski, Marc E. Pfetsch
2010	J	`jnl`	Symmetry Breaking with Polynomial Delay CoRR Tim Januschowski, Barbara M. Smith, Marc R. C. van Dongen