A Critical Audit of Spatiotemporal Forecasting Benchmark Datasets and Models
This research audits popular forecasting benchmarks to find biases. It analyzes correlations and critiques evaluation methods, revealing that many studies use flawed protocols like differencing. The findings suggest spatially-unaware linear models often outperform complex Graph Neural Networks on certain datasets, demanding more rigorous statistical comparison.