368 citations · 635 across the 21 of their papers we have counts for
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stat.ML2017★ 12 cited
Approximate Bayesian Inference in Linear State Space Models for Intermittent Demand Forecasting at Scale
Matthias Seeger, Syama Rangapuram, Yuyang Wang +4
We present a scalable and robust Bayesian inference method for linear state space models. The method is applied to demand forecasting in the context of a large e-commerce platform,…
cs.AI2017
DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks
David Salinas, Valentin Flunkert, Jan Gasthaus
Probabilistic forecasting, i.e. estimating the probability distribution of a time series' future given its past, is a key enabler for optimizing business processes. In retail busin…