| TITAN |
- MAE @ 12 step
- 3.08
- 12 steps MAE
- 3.08
- 12 steps RMSE
- 6.21
- 12 steps MAPE
- 8.43
- MAE @ 3 step
- 2.41
|
A Time Series is Worth Five Experts: Heterogeneous Mixture of Experts for Traffic Flow Prediction 2024-09-26 |
코드 1 |
| T-Graphormer |
- MAE @ 12 step
- 3.19
- 12 steps MAE
- 3.19
- 12 steps RMSE
- 6.12
- 12 steps MAPE
- 8.62
- MAE @ 3 step
- 2.63
|
T-Graphormer: Using Transformers for Spatiotemporal Forecasting 2025-01-22 |
코드 1 |
| STGM |
- MAE @ 12 step
- 3.229
- 12 steps MAE
- 3.229
- 12 steps RMSE
- 7.099
- 12 steps MAPE
- 9.39
|
Spatio-Temporal Graph Mixformer for Traffic Forecasting 2023-10-15 |
코드 1 |
| Traffic Transformer |
- MAE @ 12 step
- 3.28
|
Traffic transformer: Capturing the continuity and periodicity of time series for traffic forecasting 2020-06-11 |
연결 없음 |
| SLCNN |
- MAE @ 12 step
- 3.3
|
Spatio-Temporal Graph Structure Learning for Traffic Forecasting 2020-04-03 |
연결 없음 |
| STAEformer |
- MAE @ 12 step
- 3.34
- MAE @ 3 step
- 2.65
|
STAEformer: Spatio-Temporal Adaptive Embedding Makes Vanilla Transformer SOTA for Traffic Forecasting 2023-08-21 |
코드 1 |
| D2STGNN |
- MAE @ 12 step
- 3.35
- MAE @ 3 step
- 2.56
|
Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting 2022-06-18 |
코드 1 |
| STEP |
- MAE @ 12 step
- 3.37
- MAE @ 3 step
- 2.61
|
Pre-training Enhanced Spatial-temporal Graph Neural Network for Multivariate Time Series Forecasting 2022-06-18 |
코드 1 · 코드 2 |
| MegaCRN |
- MAE @ 12 step
- 3.38
- MAE @ 3 step
- 2.63
|
Spatio-Temporal Meta-Graph Learning for Traffic Forecasting 2022-11-27 |
코드 1 |
| STD-MAE |
- MAE @ 12 step
- 3.40
- 12 steps MAE
- 3.40
- 12 steps RMSE
- 7.07
- 12 steps MAPE
- 9.59
- MAE @ 3 step
- 2.62
|
Spatial-Temporal-Decoupled Masked Pre-training for Spatiotemporal Forecasting 2023-12-01 |
코드 1 |
| RGDAN |
- MAE @ 12 step
- 3.40
- MAE @ 3 step
- 2.69
|
RGDAN: A random graph diffusion attention network for traffic prediction 2024-01-16 |
코드 1 |
| ADN |
- MAE @ 12 step
- 3.42
|
Structured Time Series Prediction without Structural Prior 2022-02-07 |
코드 1 |
| STAWnet |
- MAE @ 12 step
- 3.44
- MAE @ 3 step
- 2.70
|
Spatial‐temporal attention wavenet: A deep learning framework for traffic prediction considering spatial‐temporal dependencies 2021-03-02 |
코드 1 |
| Finetune from t1-6 checkpoint |
- MAE @ 12 step
- 3.47
|
Incrementally Improving Graph WaveNet Performance on Traffic Prediction 2019-12-11 |
코드 1 · 코드 2 · 코드 3 · 코드 4 |
| DCGCN |
- MAE @ 12 step
- 3.48
- 12 steps MAE
- 3.48
- 12 steps RMSE
- 6.94
- 12 steps MAPE
- 9.94
|
Dynamic Causal Graph Convolutional Network for Traffic Prediction 2023-06-12 |
코드 1 |
| GWNET-Cov |
- MAE @ 12 step
- 3.50
- MAE @ 3 step
- 2.69
|
Conditional Temporal Neural Processes with Covariance Loss 2021-06-22 |
코드 1 |
| Graph WaveNet |
- MAE @ 12 step
- 3.53
- MAE @ 3 step
- 2.69
|
Graph WaveNet for Deep Spatial-Temporal Graph Modeling 2019-05-31 |
코드 1 · 코드 2 · 코드 3 · 코드 4 · 코드 5 · 코드 6 · 코드 7 · 코드 8 · 코드 9 |
| ST-UNet |
- MAE @ 12 step
- 3.55
|
ST-UNet: A Spatio-Temporal U-Network for Graph-structured Time Series Modeling 2019-03-13 |
연결 없음 |
| DCRNN |
- MAE @ 12 step
- 3.6
- MAE @ 3 step
- 2.77
|
Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting 2017-07-06 |
코드 1 · 코드 2 · 코드 3 · 코드 4 · 코드 5 · 코드 6 · 코드 7 · 코드 8 · 코드 9 · 코드 10 · 코드 11 · 코드 12 · 코드 13 · 코드 14 · 코드 15 · 코드 16 · 코드 17 · 코드 18 · 코드 19 |
| STGCN |
- MAE @ 12 step
- 4.45
|
Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting 2017-09-14 |
코드 1 · 코드 2 · 코드 3 · 코드 4 · 코드 5 · 코드 6 · 코드 7 |