Publications (30)
The Effectiveness of Discretization in Forecasting: An Empirical Study on Neural Time Series Models
Stephan Rabanser, Tim Januschowski, Valentin Flunkert +2
Tricks from the Trade for Large-Scale Markdown Pricing: Heuristic Cut Generation for Lagrangian Decomposition
Robert Streeck, Torsten Gellert, Andreas Schmitt +4
Approximate Bayesian Inference in Linear State Space Models for Intermittent Demand Forecasting at Scale
Matthias Seeger, Syama Rangapuram, Yuyang Wang +4
Anomaly Detection at Scale: The Case for Deep Distributional Time Series Models
Fadhel Ayed, Lorenzo Stella, Tim Januschowski +1
High-Frequency Pricing at Scale for E-Commerce
Stefan Birr, Tobias Huelden, Mones Raslan +7
Deep Factors for Forecasting
Yuyang Wang, Alex Smola, Danielle C. Maddix +3
Deep Learning for Time Series Forecasting: Tutorial and Literature Survey
Konstantinos Benidis, Syama Sundar Rangapuram, Valentin Flunkert +10
Multivariate Time Series Forecasting with Latent Graph Inference
Victor Garcia Satorras, Syama Sundar Rangapuram, Tim Januschowski
Detecting Anomalous Event Sequences with Temporal Point Processes
Oleksandr Shchur, Ali Caner Türkmen, Tim Januschowski +2
Deep Explicit Duration Switching Models for Time Series
Abdul Fatir Ansari, Konstantinos Benidis, Richard Kurle +5
A simple and effective predictive resource scaling heuristic for large-scale cloud applications
Valentin Flunkert, Quentin Rebjock, Joel Castellon +2
Deep Non-Parametric Time Series Forecaster
Syama Sundar Rangapuram, Jan Gasthaus, Lorenzo Stella +4
Online false discovery rate control for anomaly detection in time series
Quentin Rebjock, BarıŠKurt, Tim Januschowski +1
A Study of Joint Graph Inference and Forecasting
Daniel Zügner, François-Xavier Aubet, Victor Garcia Satorras +3
On the detrimental effect of invariances in the likelihood for variational inference
Richard Kurle, Ralf Herbrich, Tim Januschowski +2
Neural Flows: Efficient Alternative to Neural ODEs
Marin Biloš, Johanna Sommer, Syama Sundar Rangapuram +2
Resilient Neural Forecasting Systems
Michael Bohlke-Schneider, Shubham Kapoor, Tim Januschowski
Intermittent Demand Forecasting with Renewal Processes
Ali Caner Turkmen, Tim Januschowski, Yuyang Wang +1
Deep Learning based Forecasting: a case study from the online fashion industry
Manuel Kunz, Stefan Birr, Mones Raslan +13
Criteria for Classifying Forecasting Methods
Tim Januschowski, Jan Gasthaus, Yuyang Wang +4
Multi-Objective Model Selection for Time Series Forecasting
Oliver Borchert, David Salinas, Valentin Flunkert +2
Neural Temporal Point Processes: A Review
Oleksandr Shchur, Ali Caner Türkmen, Tim Januschowski +1
Intrinsic Anomaly Detection for Multi-Variate Time Series
Stephan Rabanser, Tim Januschowski, Kashif Rasul +6
Forecasting: theory and practice
Fotios Petropoulos, Daniele Apiletti, Vassilios Assimakopoulos +77
Intermittent Demand Forecasting with Deep Renewal Processes
Ali Caner Turkmen, Yuyang Wang, Tim Januschowski
Causal Forecasting for Pricing
Douglas Schultz, Johannes Stephan, Julian Sieber +4
Meta-Forecasting by combining Global Deep Representations with Local Adaptation
Riccardo Grazzi, Valentin Flunkert, David Salinas +3
Diverse Counterfactual Explanations for Anomaly Detection in Time Series
Deborah Sulem, Michele Donini, Muhammad Bilal Zafar +6
GluonTS: Probabilistic Time Series Models in Python
Alexander Alexandrov, Konstantinos Benidis, Michael Bohlke-Schneider +10
Multivariate Quantile Function Forecaster
Kelvin Kan, François-Xavier Aubet, Tim Januschowski +4