djm209 / HSTGODE
HSTGODE code
☆9Updated last year
Alternatives and similar repositories for HSTGODE:
Users that are interested in HSTGODE are comparing it to the libraries listed below
- An efficient spatial-temporal transformer with temporal aggregation and spatial memory for traffic forecasting☆23Updated last week
- [KDD'24] Official code for our paper "Heterogeneity-Informed Meta-Parameter Learning for Spatiotemporal Time Series Forecasting".☆50Updated 8 months ago
- [ICDE 2023] Dynamic hypergraph structure learning for traffic flow forecasting☆15Updated 2 years ago
- SPGCL: Mining Spatio-Temporal Relations via Self-Paced Graph Contrastive Learning☆14Updated 2 years ago
- [TITS2025] Pattern-Matching Dynamic Memory Network for Dual-Mode Traffic Prediction☆18Updated 2 weeks ago
- ☆15Updated last year
- ☆47Updated 2 years ago
- [Neural Networks] PDG2Seq: Periodic Dynamic Graph to Sequence model for Traffic Flow Prediction☆13Updated last month
- ☆44Updated 2 years ago
- The offical code for 《A Time Series is Worth Five Experts: Heterogeneous Mixture of Experts for Traffic Flow Prediction》☆22Updated 7 months ago
- This is an implementation of a traffic speed predicition model.☆10Updated 2 years ago
- ☆15Updated last year
- ☆25Updated last year
- source codes of TASSGN☆11Updated last year
- [WWW'2023] "AutoST: Automated Spatio-Temporal Graph Contrastive Learning"☆65Updated last year
- [TKDE 2022] The official PyTorch implementation of the paper "Multivariate Time Series Forecasting with Dynamic Graph Neural ODEs".☆82Updated 2 years ago
- Spatial-Temporal Graph ODE Neural Network☆109Updated 3 years ago
- ☆38Updated 2 years ago
- ☆19Updated 3 years ago
- The official implementation of SDGL☆32Updated 9 months ago
- Code of “STG4Traffic: A Survey and Benchmark of Spatial-Temporal Graph Neural Networks for Traffic Prediction”.☆69Updated 9 months ago
- This is the official release code of AAAI2023 accepted paper: "Spatial temporal Neural Structural Causal Models for Bike Flow Prediction"☆36Updated 2 years ago
- ☆10Updated last year
- AGC-Net (Adaptive Graph Convolution Networks) is an advanced model designed to predict traffic flow. Dataset: METR-LA, PEMS-BAY☆11Updated last year
- When Do Contrastive Learning Signals Help Spatio-Temporal Graph Forecasting? (SIGSPATIAL 2022)☆42Updated 2 years ago
- [SIGKDD'2025] Efficient Large-Scale Traffic Forecasting with Transformers: A Spatial Data Management Perspective☆36Updated 4 months ago
- Official repository for the paper "Scalable Spatiotemporal Graph Neural Networks" (AAAI 2023)☆46Updated last year
- ☆13Updated 5 months ago
- Code of “Dynamic Graph Convolutional Network with Attention Fusion for Traffic Flow Prediction”.☆16Updated last year
- Code for the TMLR 2023 paper "GRAM-ODE: Graph-based Multi-ODE Neural Networks for Spatio-Temporal Traffic Forecasting"☆16Updated last year