368 citations · 635 across the 21 of their papers we have counts for
3 papers · 1 filter
High-Dimensional Multivariate Forecasting with Low-Rank Gaussian Copula Processes
David Salinas, Michael Bohlke-Schneider, Laurent Callot +2
Predicting the dependencies between observations from multiple time series is critical for applications such as anomaly detection, financial risk management, causal analysis, or de…
GluonTS: Probabilistic Time Series Models in Python
Alexander Alexandrov, Konstantinos Benidis, Michael Bohlke-Schneider +10
We introduce Gluon Time Series (GluonTS, available at https://gluon-ts.mxnet.io), a library for deep-learning-based time series modeling. GluonTS simplifies the development of and…
Deep Factors for Forecasting
Yuyang Wang, Alex Smola, Danielle C. Maddix +3
Producing probabilistic forecasts for large collections of similar and/or dependent time series is a practically relevant and challenging task. Classical time series models fail to…