Demand Forecasting

28 papers with code • 1 benchmarks • 1 datasets

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Datasets


Most implemented papers

Graph Neural Network for Traffic Forecasting: A Survey

jwwthu/GNN4Traffic 27 Jan 2021

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

underdoc-wang/ST-MGCN Conference 2019

This task is challenging due to the complicated spatiotemporal dependencies among regions.

Demand Forecasting from Spatiotemporal Data with Graph Networks and Temporal-Guided Embedding

LeeDoYup/TGNet-keras 26 May 2019

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

CIOL-SUST/SCG 13 Nov 2024

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

mbohlkeschneider/gluon-ts NeurIPS 2019

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

moonfolk/MiFM NeurIPS 2017

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

rubikloud/matrnn 29 Dec 2018

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

rajatsen91/deepglo NeurIPS 2019

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

LeiBAI/STG2Seq 24 May 2019

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

underdoc-wang/ST-MGCN AAAI 2019

This task is challenging due to the complicated spatiotemporal dependencies among regions.