scholar.google.com › citations
Jan 23, 2024 · This paper builds on previous research on GNNs and makes two contributions. First, we integrate a GNN encoder into a state-of-the-art DeepAR ...
We introduce an end-to-end GraphDeepAR model that provides probabilistic demand predictions and avoids reliance on a pre-defined graph structure for graph ...
Abstract. Demand forecasting is a prominent business use case that al- lows retailers to optimize inventory planning, logistics, and core business.
Sep 22, 2023 · Demand forecasting in e-Commerce o Literature remains rather limited. ▫ Key challenges: high dimensionality, no pre-defined graph structure.
Jan 23, 2024 · This paper integrates a GNN encoder into a state-of-the-art DeepAR model and proposes to build graphs using article attribute similarity, ...
Probabilistic Demand Forecasting with Graph Neural Networks ... We also show that our approach produces article embeddings that encode article similarity and ...
People also search for
In this work, we propose a deep hybrid probabilistic graph-based forecasting framework called Graph Deep Factors (GraphDF) that goes beyond these two extremes.
People also ask
Can neural networks be used for forecasting?
Which algorithm is best for demand forecasting?
What is probabilistic forecasting in supply chain?
What are the three levels of demand forecasting?
It consists of two components: the Mobility Forecasting Module based on Graph-based Network and the. Bayesian Approximation Module based on Monte Carlo ...
Dec 12, 2024 · We introduce a novel framework called Graph-based Denoising Diffusion Probabilistic Model (G-DDPM) for probabilistic forecasting of multivariate ...
Here you can find my machine learning research papers, MSc thesis, and EngD thesis. 2023 Probabilistic Demand Forecasting with Graph Neural Networks