mlsanityData to 14 Oct 2026

Minimalist Traffic Prediction: Linear Layer Is All You Need

arXiv 2308.10276 ↗First posted 20 Aug 2023cs.LGcited by 0

Not on the map. Why?

Abstract (arXiv)

Traffic prediction is essential for the progression of Intelligent Transportation Systems (ITS) and the vision of smart cities. While Spatial-Temporal Graph Neural Networks (STGNNs) have shown promise in this domain by leveraging Graph Neural Networks (GNNs) integrated with either RNNs or Transformers, they present challenges such as computational complexity, gradient issues, and resource-intensiveness. This paper addresses these challenges, advocating for three main solutions: a node-embedding approach, time series decomposition, and periodicity learning. We introduce STLinear, a minimalist model architecture designed for optimized efficiency and performance. Unlike traditional STGNNs, STlinear operates fully locally, avoiding inter-node data exchanges, and relies exclusively on linear layers, drastically cutting computational demands. Our empirical studies on real-world datasets confirm STLinear's prowess, matching or exceeding the accuracy of leading STGNNs, but with significantly reduced complexity and computation overhead (more than 95% reduction in MACs per epoch compared to state-of-the-art STGNN baseline published in 2023). In summary, STLinear emerges as a potent, efficient alternative to conventional STGNNs, with profound implications for the future of ITS and smart city initiatives.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 16 of its 30 references that are in the map. First posted on arXiv 20 Aug 2023.

Builds on

16 of 30 references.

References · 16

  1. 2017Attention Is All You Needcited by 60,361
  2. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  3. 2020Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecastingcited by 2,169
  4. 2022Are Transformers Effective for Time Series Forecasting?cited by 1,424
  5. 2017Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecastingcited by 1,136
  6. 2019Graph WaveNet for Deep Spatial-Temporal Graph Modelingcited by 829
  7. 2020Adaptive Graph Convolutional Recurrent Network for Traffic Forecastingcited by 530
  8. 2019GMAN: A Graph Multi-Attention Network for Traffic Predictioncited by 434
  9. 2020Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecastingcited by 213
  10. 2022Less Is More: Fast Multivariate Time Series Forecasting with Light Sampling-oriented MLP Structurescited by 160
  11. 2022Spatial-Temporal Identity: A Simple yet Effective Baseline for Multivariate Time Series Forecastingcited by 151
  12. 2023TSMixer: Lightweight MLP-Mixer Model for Multivariate Time Series Forecastingcited by 139
4 more
  1. 2023PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for Traffic Flow Predictioncited by 137
  2. 2021Graph Neural Controlled Differential Equations for Traffic Forecastingcited by 133
  3. 2023MTS-Mixers: Multivariate Time Series Forecasting via Factorized Temporal and Channel Mixingcited by 53
  4. 2023Localised Adaptive Spatial-Temporal Graph Neural Networkcited by 14

Built on by

Not cited yet.