77 citations · 229 across the 17 of their papers we have counts for
6 papers · 1 filter
Criteria for Classifying Forecasting Methods
Tim Januschowski, Jan Gasthaus, Yuyang Wang +4
Classifying forecasting methods as being either of a "machine learning" or "statistical" nature has become commonplace in parts of the forecasting literature and community, as exem…
On the detrimental effect of invariances in the likelihood for variational inference
Richard Kurle, Ralf Herbrich, Tim Januschowski +2
Variational Bayesian posterior inference often requires simplifying approximations such as mean-field parametrisation to ensure tractability. However, prior work has associated the…
Diverse Counterfactual Explanations for Anomaly Detection in Time Series
Deborah Sulem, Michele Donini, Muhammad Bilal Zafar +6
Data-driven methods that detect anomalies in times series data are ubiquitous in practice, but they are in general unable to provide helpful explanations for the predictions they m…
Resilient Neural Forecasting Systems
Michael Bohlke-Schneider, Shubham Kapoor, Tim Januschowski
Industrial machine learning systems face data challenges that are often under-explored in the academic literature. Common data challenges are data distribution shifts, missing valu…
Multivariate Time Series Forecasting with Latent Graph Inference
Victor Garcia Satorras, Syama Sundar Rangapuram, Tim Januschowski
This paper introduces a new approach for Multivariate Time Series forecasting that jointly infers and leverages relations among time series. Its modularity allows it to be integrat…
Multi-Objective Model Selection for Time Series Forecasting
Oliver Borchert, David Salinas, Valentin Flunkert +2
Research on time series forecasting has predominantly focused on developing methods that improve accuracy. However, other criteria such as training time or latency are critical in…