A Critical Audit of Spatiotemporal Forecasting Benchmark Datasets and Baselines

• URL: http://arxiv.org/abs/2608.20980v1
• Date: Fri, 21 Aug 2026 11:08:17 GMT
• Title: A Critical Audit of Spatiotemporal Forecasting Benchmark Datasets and Baselines
• Abstract summary: We take a step back and analyse the benchmark datasets via classical time series methods to uncover why spatially-unaware linear models pose a stronger competitor than previously reported.<n>Our statistical analysis provides a toolset for identifying significant spatial and temporal correlations.<n>By applying the results of our analysis to a simple hybrid model, we show how our methodology can lead to novel ways of developing GNN models.
• Score: 19.152026148047536
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Graph neural networks (GNNs) are routinely employed for short-range forecasting on multivariate time series with a spatial graph structure. Despite the availability of many alternative datasets, method innovations within this domain are predominantly assessed against a rather limited set of benchmark datasets, most notably Chickenpox, PedalMe, WikiMaths, METR-LA, and PEMS-BAY. The evaluation protocols contain baselines spanning from historical averages to classical machine learning approaches. These baselines often show competitive performance compared to GNNs. In the present work, we take a step back and analyse the benchmark datasets via classical time series methods to uncover why spatially-unaware linear models pose a stronger competitor than previously reported, casting further doubt on the discriminative reliability of the aforementioned widely adopted datasets. Our statistical analysis provides a toolset for identifying significant spatial and temporal correlations, while revealing a structural bias introduced by first-order differenced datasets. We therefore recommend reducing the over-reliance on such datasets for method comparison, and instead advocate for more rigorous statistical evaluation. By applying the results of our analysis to a simple hybrid model, we show how our methodology can lead to novel ways of developing GNN models

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