368 citations · 635 across the 21 of their papers we have counts for
6 papers · 1 filter
Monte Carlo EM for Deep Time Series Anomaly Detection
François-Xavier Aubet, Daniel Zügner, Jan Gasthaus
Time series data are often corrupted by outliers or other kinds of anomalies. Identifying the anomalous points can be a goal on its own (anomaly detection), or a means to improving…
Learning Quantile Functions without Quantile Crossing for Distribution-free Time Series Forecasting
Youngsuk Park, Danielle Maddix, François-Xavier Aubet +3
Quantile regression is an effective technique to quantify uncertainty, fit challenging underlying distributions, and often provide full probabilistic predictions through joint lear…
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…
PSA-GAN: Progressive Self Attention GANs for Synthetic Time Series
Jeha Paul, Bohlke-Schneider Michael, Mercado Pedro +5
Realistic synthetic time series data of sufficient length enables practical applications in time series modeling tasks, such as forecasting, but remains a challenge. In this paper…
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…
Detecting Anomalous Event Sequences with Temporal Point Processes
Oleksandr Shchur, Ali Caner Türkmen, Tim Januschowski +2
Automatically detecting anomalies in event data can provide substantial value in domains such as healthcare, DevOps, and information security. In this paper, we frame the problem o…