Graph-based models have also emerged as a promising avenue for water demand forecasting. A study developed a graph convolutional recurrent neural network (GCRNN) that captures both spatial and ...

Tourism demand forecasting is an essential aspect of managing ... For instance, a novel model called the spatial-temporal fused graph convolutional network (ST-FGCN) was developed to capture ...

Link Guo K, Hu Y, Qian Z S, et al. An Optimized Temporal-Spatial Gated Graph Convolution Network for Traffic Forecasting[J]. IEEE Intelligent Transportation Systems Magazine, 2020. Link Luo M, Du B, ...

Demand Forecasting Best Practices teaches you to optimize demand planning to deliver a more effective supply chain. In this unique step-by-step guide, you’ll learn forecasting tools, metrics, and ...

Demand forecasting is the process of estimating the future demand for your products or services based on historical data, market trends, customer feedback, and other factors. Demand forecasting ...

A data scientist and entrepreneur with a background in finance and physics. Accurate demand forecasting is the linchpin of effective inventory, cost management and readiness. Knowing how much ...

Demand forecasting is a crucial process for logistics management, as it helps to plan inventory, transportation, and distribution activities. However, demand forecasting is also subject to ...

Elasticity Of Demand,Electric Vehicles,Exhaustive Search,Fast Charging Stations,Forecasting,Graph Convolution,Graph Convolutional Network,Graph Neural Networks,Graph Signal,Heuristic ...