368 citations · 725 across the 24 of their papers we have counts for
4 papers · 1 filter
Intermittent Demand Forecasting with Deep Renewal Processes
Ali Caner Turkmen, Yuyang Wang, Tim Januschowski
Intermittent demand, where demand occurrences appear sporadically in time, is a common and challenging problem in forecasting. In this paper, we first make the connections between…
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…
The GSVD: Where are the ellipses?, Matrix Trigonometry, and more
Alan Edelman, Yuyang Wang
This paper provides an advanced mathematical theory of the Generalized Singular Value Decomposition (GSVD) and its applications. We explore the geometry of the GSVD which provides…