2024年1月23日 · First, we integrate a GNN encoder into a state-of-the-art DeepAR model. The combined model produces probabilistic forecasts, which are crucial for decision-making under …

Create and insert a demand forecast model (using actual or fictional data). To demonstrate effective analytical skills, explain how you would communicate the demand forecast to senior …

2024年11月4日 · Traditional statistical models and deep learning methods often face challenges in efficiently discerning correlations within a myriad of interconnected demands. To tackle this …

2024年1月23日 · This paper proposes a novel demand forecasting approach that leverages Graph Neural Networks (GNNs) to account for article relationships during training and …

2019年5月26日 · Abstract: Short-term demand forecasting models commonly combine convolutional and recurrent layers to extract complex spatiotemporal patterns in data. Long …

We test GraphDeepAR on three real-world datasets and show that it outperforms the standard DeepAR model. Forecasting refers to prediction of future events based on the previously …

2024年6月27日 · In this article we will delve into how you can safeguard demand forecasting (or any forecasting use case to be honest) with causal graphs. The following areas will be …

2021年3月15日 · Demand Forecasting is the pivotal business process around which strategic and operational plans of a company are devised. Based on the Demand Forecast, strategic and …

propose a general demand forecasting framework for multivariate corre-lated time series. The proposed technique models the homogeneous and heterogeneous correlations between …

2022年1月19日 · In this study, we propose a demand forecasting model that forecasts demand with the usage of graph-based product embeddings. Unlike most of the existing methods, the …