arXiv Machine Learning
GraphToolbox: A Configurable Python Framework for Graph Neural Network Forecasting
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arXiv AI
Oct 27
A Diagnostic Protocol for GNNs in Electricity Load Forecasting: Separating Parameter Sharing from Neighborhood Access
The paper introduces a diagnostic protocol to disentangle the contributions of parameter sharing and neighborhood access in graph neural networks (GNNs) for electricity load forecasting. By comparing a GNN to three reference models—per-node, shared-parameter without neighbors, and the same GNN without edges—the study evaluates the impact of graph structure across datasets from France, UK feeders, and client consumption series. Results show that parameter sharing often accounts for most of the performance gain, while edges contribute less and can be diminished by degree-preserving rewiring.
arXiv Machine Learning
Oct 7
A Critical Audit of Spatiotemporal Forecasting Benchmark Datasets and Models
arXiv:2608.20980v2 Announce Type: replace Abstract: Graph neural networks (GNNs) are routinely employed for spatiotemporal forecasting, yet their performance across widely used benchmark datasets is...
arXiv Statistics ML
Oct 27
How Much Graph Is Too Much? Graph Construction and Exposure in Spatio-Temporal Forecasting
arXiv:2610.34611v1 Announce Type: new Abstract: Spatio-temporal graph neural networks combine a node's history with information from graph neighbors. The central question is whether graph neighbors p...
arXiv Machine Learning
Aug 24
A Critical Audit of Spatiotemporal Forecasting Benchmark Datasets and Baselines
arXiv:2608.20980v1 Announce Type: new Abstract: Graph neural networks (GNNs) are routinely employed for short-range forecasting on multivariate time series with a spatial graph structure. Despite the...
arXiv Machine Learning
Aug 21
Multi-Modal Graph Interaction for Multi-Graph Convolution Network in Urban Spatiotemporal Forecasting
arXiv:1905. 11395v2 Announce Type: replace Abstract: Graph convolution network based approaches have been recently used to model region-wise relationships in region-level prediction problems in urban computing.
arXiv Machine Learning
Aug 20
An Empirical Benchmark of Deep Time-Series Models for Smart Meter Energy Forecasting
The paper presents an empirical benchmark of nine modern deep‑learning models for time‑series forecasting of smart‑meter energy consumption, evaluated on two publicly available datasets. It examines how historical input length, prediction horizon, and model architecture affect accuracy, finding that longer historical context improves performance up to a saturation point and that accuracy declines with longer horizons. The study also compares computational complexity, showing that lightweight architectures achieve similar performance to heavier models, and notes that model choice has limited impact across most demographic and household subgroups.