77 citations · 210 across the 12 of their papers we have counts for
12 papers
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…
Online Time Series Anomaly Detection with State Space Gaussian Processes
Christian Bock, François-Xavier Aubet, Jan Gasthaus +3
We propose r-ssGPFA, an unsupervised online anomaly detection model for uni- and multivariate time series building on the efficient state space formulation of Gaussian processes. F…
A Study of Joint Graph Inference and Forecasting
Daniel Zügner, François-Xavier Aubet, Victor Garcia Satorras +3
We study a recent class of models which uses graph neural networks (GNNs) to improve forecasting in multivariate time series. The core assumption behind these models is that there…
Neural Contextual Anomaly Detection for Time Series
Chris U. Carmona, François-Xavier Aubet, Valentin Flunkert +1
We introduce Neural Contextual Anomaly Detection (NCAD), a framework for anomaly detection on time series that scales seamlessly from the unsupervised to supervised setting, and is…