# Spatio-Temporal Neural Networks for Space-Time Series Forecasting and Relations Discovery

arXiv: https://arxiv.org/abs/1804.08562 · HTML: https://mlsanity.com/paper/1804.08562/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Apr 2018.

## Built on by (most cited first, 16 of 16)

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- [FedST: Secure Federated Shapelet Transformation for Time Series Classification](https://mlsanity.com/paper/2302.10631/) (2023)
- [Spatio-Temporal Neural Network for Fitting and Forecasting COVID-19](https://mlsanity.com/paper/2103.11860/) (2021)
- [GSA-Forecaster: Forecasting Graph-Based Time-Dependent Data with Graph Sequence Attention](https://mlsanity.com/paper/2104.05914/) (2021)
- [Deep Geospatial Interpolation Networks](https://mlsanity.com/paper/2108.06670/) (2021)
- [Modeling Oceanic Variables with Dynamic Graph Neural Networks](https://mlsanity.com/paper/2206.12746/) (2022)
- [Temporal-Spatial dependencies ENhanced deep learning model (TSEN) for household leverage series forecasting](https://mlsanity.com/paper/2210.08668/) (2022)
- [STEF-DHNet: Spatiotemporal External Factors Based Deep Hybrid Network for Enhanced Long-Term Taxi Demand Prediction](https://mlsanity.com/paper/2306.14476/) (2023)
- [Enhancing Graph Neural Networks Using Proximity Graphs for Dust Source Emission Forecasting](https://mlsanity.com/paper/2606.19825/) (2026)

## Abstract (arXiv)

We introduce a dynamical spatio-temporal model formalized as a recurrent neural network for forecasting time series of spatial processes, i.e. series of observations sharing temporal and spatial dependencies. The model learns these dependencies through a structured latent dynamical component, while a decoder predicts the observations from the latent representations. We consider several variants of this model, corresponding to different prior hypothesis about the spatial relations between the series. The model is evaluated and compared to state-of-the-art baselines, on a variety of forecasting problems representative of different application areas: epidemiology, geo-spatial statistics and car-traffic prediction. Besides these evaluations, we also describe experiments showing the ability of this approach to extract relevant spatial relations.