AdaST: Adaptive Coupling for Spatial-Temporal Forecasting
cs.AI
Submitted: 2026-09-28
Updated: 2026-09-28
Code: https://github.com/LzyFischer/AdaST
Sources
- Long-Range Transformers for Dynamic Spatiotemporal Forecasting
- Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting
- STAEformer: Spatio-Temporal Adaptive Embedding Makes Vanilla Transformer SOTA for Traffic Forecasting
- A Time Series is Worth 64 Words: Long-term Forecasting with Transformers
- N-BEATS: Neural basis expansion analysis for interpretable time series forecasting
- Discrete Graph Structure Learning for Forecasting Multiple Time Series
- Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting
- Graph WaveNet for Deep Spatial-Temporal Graph Modeling
- A Survey on Diffusion Models for Time Series and Spatio-Temporal Data
- Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting
- GraphIP-Bench: How Hard Is It to Steal a Graph Neural Network, and Can We Stop It?
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