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Spatio-Temporal Graph Convolutional Networks: Optimised Temporal Architecture

arXiv 2501.10454 ↗First posted 14 Jan 2025cs.LG stat.MLcited by 0

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Abstract (arXiv)

Spatio-Temporal graph convolutional networks were originally introduced with CNNs as temporal blocks for feature extraction. Since then LSTM temporal blocks have been proposed and shown to have promising results. We propose a novel architecture combining both CNN and LSTM temporal blocks and then provide an empirical comparison between our new and the pre-existing models. We provide theoretical arguments for the different temporal blocks and use a multitude of tests across different datasets to assess our hypotheses.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 14 of its 20 references that are in the map. First posted on arXiv 14 Jan 2025.

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14 of 20 references.

References · 14

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  2. 2016Convolutional Neural Networks on Graphs with Fast Localized Spectral Filteringcited by 2,089
  3. 2016Variational Graph Auto-Encoderscited by 1,382
  4. 2018Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognitioncited by 1,209
  5. 2017Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecastingcited by 1,136
  6. 2019Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directionscited by 172
  7. 2021PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Modelscited by 118
  8. 2022Convolutional Neural Networks on Graphs with Chebyshev Approximation, Revisitedcited by 87
  9. 2017Comparative Study of CNN and RNN for Natural Language Processingcited by 82
  10. 2019STEP: Spatial Temporal Graph Convolutional Networks for Emotion Perception from Gaitscited by 36
  11. 2021Spatio-temporal graph neural networks for multi-site PV power forecastingcited by 24
  12. 2020Stock Price Prediction Using CNN and LSTM-Based Deep Learning Modelscited by 21
2 more
  1. 2019Recurrent Neural Networks for Time Series Forecastingcited by 19
  2. 2021Process Outcome Prediction: CNN vs. LSTM (with Attention)cited by 12

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