# Echo State Networks for Spatio-Temporal Area-Level Data

arXiv: https://arxiv.org/abs/2410.10641 · HTML: https://mlsanity.com/paper/2410.10641/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 41 references that are in the map.
- First posted on arXiv 14 Oct 2024.

## Builds on (most cited first, 8 of 8)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Simplifying Graph Convolutional Networks](https://mlsanity.com/paper/1902.07153/) (2019)
- [Random Features for Kernel Approximation: A Survey on Algorithms, Theory, and Beyond](https://mlsanity.com/paper/2004.11154/) (2020)
- [Deep Echo State Networks with Uncertainty Quantification for Spatio-Temporal Forecasting](https://mlsanity.com/paper/1806.10728/) (2018)
- [An Ensemble Quadratic Echo State Network for Nonlinear Spatio-Temporal Forecasting](https://mlsanity.com/paper/1708.05094/) (2017)
- [Using Echo State Networks to Inform Physical Models for Fire Front Propagation](https://mlsanity.com/paper/2302.04960/) (2023)
- [Spatial Fay-Herriot Models for Small Area Estimation with Functional Covariates](https://mlsanity.com/paper/1303.6668/) (2013)
- [Small Area Estimation via Multivariate Fay-Herriot Models with Latent Spatial Dependence](https://mlsanity.com/paper/1310.7211/) (2013)

## Built on by (most cited first, 2 of 2)

- [CESAR: A Convolutional Echo State AutoencodeR for High-Resolution Wind Forecasting](https://mlsanity.com/paper/2412.10578/) (2024)
- [Hierarchical Count Echo State Network Models with Application to Graduate Student Enrollments](https://mlsanity.com/paper/2501.14698/) (2025)

## Abstract (arXiv)

Spatio-temporal area-level datasets play a critical role in official statistics, providing valuable insights for policy-making and regional planning. Accurate modeling and forecasting of these datasets can be extremely useful for policymakers to develop informed strategies for future planning. Echo State Networks (ESNs) are efficient methods for capturing nonlinear temporal dynamics and generating forecasts. However, ESNs lack a direct mechanism to account for the neighborhood structure inherent in area-level data. Ignoring these spatial relationships can significantly compromise the accuracy and utility of forecasts. In this paper, we incorporate approximate graph spectral filters at the input stage of the ESN, thereby improving forecast accuracy while preserving the model's computational efficiency during training. We demonstrate the effectiveness of our approach using Eurostat's tourism occupancy dataset and show how it can support more informed decision-making in policy and planning contexts.