77 citations · 229 across the 17 of their papers we have counts for
3 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…