# Graph-Based Deep Modeling and Real Time Forecasting of Sparse Spatio-Temporal Data

arXiv: https://arxiv.org/abs/1804.00684 · HTML: https://mlsanity.com/paper/1804.00684/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Apr 2018.

## Built on by (most cited first, 10 of 10)

- [Graph Neural Network for Traffic Forecasting: A Survey](https://mlsanity.com/paper/2101.11174/) (2021)
- [Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Survey](https://mlsanity.com/paper/2303.14483/) (2023)
- [Graph Neural Networks for Traffic Forecasting](https://mlsanity.com/paper/2104.13096/) (2021)
- [A Study of Joint Graph Inference and Forecasting](https://mlsanity.com/paper/2109.04979/) (2021)
- [Crime Prediction with Graph Neural Networks and Multivariate Normal Distributions](https://mlsanity.com/paper/2111.14733/) (2021)
- [FusionTransNet for Smart Urban Mobility: Spatiotemporal Traffic Forecasting Through Multimodal Network Integration](https://mlsanity.com/paper/2405.05786/) (2024)
- [A Survey on Societal Event Forecasting with Deep Learning](https://mlsanity.com/paper/2112.06345/) (2021)
- [Deep Learning-Based Spatiotemporal Multi-Event Reconstruction for Delay Line Detectors](https://mlsanity.com/paper/2306.09359/) (2023)
- [WEST GCN-LSTM: Weighted Stacked Spatio-Temporal Graph Neural Networks for Regional Traffic Forecasting](https://mlsanity.com/paper/2405.00570/) (2024)
- [Graph Based Traffic Analysis and Delay Prediction](https://mlsanity.com/paper/2410.21028/) (2024)

## Abstract (arXiv)

We present a generic framework for spatio-temporal (ST) data modeling, analysis, and forecasting, with a special focus on data that is sparse in both space and time. Our multi-scaled framework is a seamless coupling of two major components: a self-exciting point process that models the macroscale statistical behaviors of the ST data and a graph structured recurrent neural network (GSRNN) to discover the microscale patterns of the ST data on the inferred graph. This novel deep neural network (DNN) incorporates the real time interactions of the graph nodes to enable more accurate real time forecasting. The effectiveness of our method is demonstrated on both crime and traffic forecasting.