Causal Forecasting for Pricing

no code implementations • 23 Dec 2023 • Douglas Schultz, Johannes Stephan, Julian Sieber, Trudie Yeh, Manuel Kunz, Patrick Doupe, Tim Januschowski

This paper proposes a novel method for demand forecasting in a pricing context.

Causal Inference Decision Making

Deep Learning based Forecasting: a case study from the online fashion industry

no code implementations • 23 May 2023 • Manuel Kunz, Stefan Birr, Mones Raslan, Lei Ma, Zhen Li, Adele Gouttes, Mateusz Koren, Tofigh Naghibi, Johannes Stephan, Mariia Bulycheva, Matthias Grzeschik, Armin Kekić, Michael Narodovitch, Kashif Rasul, Julian Sieber, Tim Januschowski

These include the volume of data, the irregularity, the high amount of turn-over in the catalog and the fixed inventory assumption.

Quantitative Universal Approximation Bounds for Deep Belief Networks

no code implementations • 18 Aug 2022 • Julian Sieber, Johann Gehringer

We show that deep belief networks with binary hidden units can approximate any multivariate probability density under very mild integrability requirements on the parental density of the visible nodes.

Slow-Fast Systems with Fractional Environment and Dynamics

no code implementations • 3 Dec 2020 • Xue-Mei Li, Julian Sieber

We prove a fractional averaging principle for interacting slow-fast systems.

Probability 60G22, 60H10, 37A25