Demand Forecasting
28 papers with code • 1 benchmarks • 1 datasets
Most implemented papers
Graph Neural Network for Traffic Forecasting: A Survey
In recent years, to model the graph structures in transportation systems as well as contextual information, graph neural networks have been introduced and have achieved state-of-the-art performance in a series of traffic forecasting problems.
Spatiotemporal Multi-Graph Convolution Networkfor Ride-hailing Demand Forecasting
This task is challenging due to the complicated spatiotemporal dependencies among regions.
Demand Forecasting from Spatiotemporal Data with Graph Networks and Temporal-Guided Embedding
TGNet learns an autoregressive model, conditioned on temporal contexts of forecasting targets from temporal-guided embedding.
Graph Neural Networks in Supply Chain Analytics and Optimization: Concepts, Perspectives, Dataset and Benchmarks
Graph Neural Networks (GNNs) have recently gained traction in transportation, bioinformatics, language and image processing, but research on their application to supply chain management remains limited.
High-Dimensional Multivariate Forecasting with Low-Rank Gaussian Copula Processes
Predicting the dependencies between observations from multiple time series is critical for applications such as anomaly detection, financial risk management, causal analysis, or demand forecasting.
Multi-way Interacting Regression via Factorization Machines
We propose a Bayesian regression method that accounts for multi-way interactions of arbitrary orders among the predictor variables.
Multivariate Arrival Times with Recurrent Neural Networks for Personalized Demand Forecasting
However, buyer purchase patterns are extremely diverse and sparse on a per-product level due to population heterogeneity as well as dependence in purchase patterns across product categories.
Think Globally, Act Locally: A Deep Neural Network Approach to High-Dimensional Time Series Forecasting
Forecasting high-dimensional time series plays a crucial role in many applications such as demand forecasting and financial predictions.
STG2Seq: Spatial-temporal Graph to Sequence Model for Multi-step Passenger Demand Forecasting
Multi-step passenger demand forecasting is a crucial task in on-demand vehicle sharing services.
Spatiotemporal Multi-Graph Convolution Network for Ride-hailing Demand Forecasting
This task is challenging due to the complicated spatiotemporal dependencies among regions.