Graphs in Motion: Spatio-Temporal Dynamics with Graph Neural Networks

📰 Medium · Data Science

Learn how to apply Graph Neural Networks to spatio-temporal dynamics for time series forecasting with a PyTorch implementation

intermediate Published 19 Apr 2026

Action Steps

Apply Graph Neural Networks to spatio-temporal dynamics
Implement a PyTorch model for time series forecasting
Use Spatio-Temporal Graph Neural Networks (ST-GNNs) for forecasting
Visualize the math behind ST-GNNs using animations
Run the PyTorch implementation for hands-on experience

Who Needs to Know This

Data scientists and machine learning engineers can benefit from this article to improve their skills in time series forecasting using graph neural networks

Key Insight

💡 Spatio-Temporal Graph Neural Networks (ST-GNNs) can be used for time series forecasting by modeling the dynamics of graphs over time