# Spatiotemporal-aware Trend-Seasonality Decomposition Network for Traffic Flow Forecasting

arXiv: https://arxiv.org/abs/2502.12213 · HTML: https://mlsanity.com/paper/2502.12213/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 33 references that are in the map.
- First posted on arXiv 17 Feb 2025.

## Builds on (most cited first, 9 of 9)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting](https://mlsanity.com/paper/2106.13008/) (2021)
- [Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting](https://mlsanity.com/paper/1709.04875/) (2017)
- [Graph WaveNet for Deep Spatial-Temporal Graph Modeling](https://mlsanity.com/paper/1906.00121/) (2019)
- [GMAN: A Graph Multi-Attention Network for Traffic Prediction](https://mlsanity.com/paper/1911.08415/) (2019)
- [Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction](https://mlsanity.com/paper/1610.00081/) (2016)
- [Spatial-Temporal Graph ODE Networks for Traffic Flow Forecasting](https://mlsanity.com/paper/2106.12931/) (2021)
- [Dynamic Graph Convolutional Recurrent Network for Traffic Prediction: Benchmark and Solution](https://mlsanity.com/paper/2104.14917/) (2021)
- [Graph Neural Controlled Differential Equations for Traffic Forecasting](https://mlsanity.com/paper/2112.03558/) (2021)

## Built on by (most cited first, 19 of 19)

- [How Different from the Past? Spatio-Temporal Time Series Forecasting with Self-Supervised Deviation Learning](https://mlsanity.com/paper/2510.04908/) (2025)
- [FedDis: A Causal Disentanglement Framework for Federated Traffic Prediction](https://mlsanity.com/paper/2601.22578/) (2026)
- [WPBench: A Comprehensive Benchmark for Wind Power Forecasting](https://mlsanity.com/paper/2609.24444/) (2026)
- [Dynamic Sheaf Diffusion Networks with Adaptive Local Structure for Heterogeneous Spatio-Temporal Graph Learning](https://mlsanity.com/paper/2604.11275/) (2026)
- [DRAN: A Distribution and Relation Adaptive Network for Spatio-temporal Forecasting](https://mlsanity.com/paper/2504.01531/) (2025)
- [Select, then Balance: Exploring Exogenous Variable Modeling of Spatio-Temporal Forecasting](https://mlsanity.com/paper/2509.05779/) (2025)
- [A Knowledge-Guided Cross-Modal Feature Fusion Model for Local Traffic Demand Prediction](https://mlsanity.com/paper/2509.06976/) (2025)
- [Efficient Linear Attention for Multivariate Time Series Modeling via Entropy Equality](https://mlsanity.com/paper/2511.03190/) (2025)
- [From Static Spectra to Operando Infrared Dynamics: Physics Informed Flow Modeling and a Benchmark](https://mlsanity.com/paper/2602.18551/) (2026)
- [Decorrelating the Future: Joint Frequency Domain Learning for Spatio-temporal Forecasting](https://mlsanity.com/paper/2603.04418/) (2026)
- [VisiFold: Long-Term Traffic Forecasting via Temporal Folding Graph and Node Visibility](https://mlsanity.com/paper/2603.11816/) (2026)
- [PHGNet: Prototype-Guided Hypergraph Construction for Heterogeneous Spatiotemporal Forecasting](https://mlsanity.com/paper/2605.25554/) (2026)
- [MoE Enhanced Federated Learning for Spatiotemporal Prediction](https://mlsanity.com/paper/2606.10499/) (2026)
- [MP3: Multi-Period Pattern Pre-training for Spatio-Temporal Forecasting](https://mlsanity.com/paper/2606.13119/) (2026)
- [STKAN: Kolmogorov-Arnold Networks for Spatio-Temporal Forecasting](https://mlsanity.com/paper/2607.13108/) (2026)
- [Every Client Is an Environment: Federated De-confounding for Spatio-Temporal Forecasting](https://mlsanity.com/paper/2607.24218/) (2026)
- [FedeRICo: Federated Region-Influenced Coupling for Traffic Flow Prediction](https://mlsanity.com/paper/2609.20026/) (2026)
- [Addressing Spatial Indistinguishability in Spatiotemporal Prediction via Optimal Transport-Guided Masking](https://mlsanity.com/paper/2609.35021/) (2026)
- [AdaST: Adaptive Coupling for Spatial-Temporal Forecasting](https://mlsanity.com/paper/2609.36119/) (2026)

## Abstract (arXiv)

Traffic prediction is critical for optimizing travel scheduling and enhancing public safety, yet the complex spatial and temporal dynamics within traffic data present significant challenges for accurate forecasting. In this paper, we introduce a novel model, the Spatiotemporal-aware Trend-Seasonality Decomposition Network (STDN). This model begins by constructing a dynamic graph structure to represent traffic flow and incorporates novel spatio-temporal embeddings to jointly capture global traffic dynamics. The representations learned are further refined by a specially designed trend-seasonality decomposition module, which disentangles the trend-cyclical component and seasonal component for each traffic node at different times within the graph. These components are subsequently processed through an encoder-decoder network to generate the final predictions. Extensive experiments conducted on real-world traffic datasets demonstrate that STDN achieves superior performance with remarkable computation cost. Furthermore, we have released a new traffic dataset named JiNan, which features unique inner-city dynamics, thereby enriching the scenario comprehensiveness in traffic prediction evaluation.