对比图神经网络与基础模型在时空预测中的权衡，揭示不同传感器密度下的最佳选择。
Evaluating Spatio-Temporal Forecasting Trade-offs Between Graph Neural Networks and Foundation Models
- 系统比较VAR、GRU、Transformer、STGNN和TSFM在不同采样频率与传感器密度下的表现
- 稀疏部署时STGNN表现更好，高频率下时间序列基础模型更优，尤其是多变量模型Moirai
- 发现跨传感器依赖关系的原生学习是性能关键，适合物联网环境下的高效预测系统设计
现代物联网环境感知系统产生大量时空数据以支持预测等下游任务，通常由机器学习模型驱动。现有边缘端数据过滤与部署策略虽优化了数据量，却忽视采样频率和空间覆盖对模型性能的影响。在许多预测模型中，增加传感器数据可借助更广的空间上下文降噪。然而，采样频率、空间覆盖与不同模型架构之间的相互作用仍不明确。本文使用真实无线传感器网络温度数据，系统评估经典模型（VAR）、神经网络（GRU、Transformer）、时空图神经网络（STGNNs）及时间序列基础模型（TSFMs：Chronos、Moirai、TimesFM）在不同传感器密度与采样间隔下的表现。结果表明：当传感器部署稀疏且采样率适中时，STGNN通过图结构编码空间相关性，有效补偿覆盖不足；而TSFMs在高频下表现优异，但邻近传感器覆盖减少时性能下降。关键发现：多变量TSFM Moirai 通过原生学习跨传感器依赖关系，超越所有模型。研究为构建高效的时空预测系统提供可操作洞察。所有代码、配置、训练日志与数据集均已开源：https://github.com/UIUC-MONET-Projects/Benchmarking-Spatiotemporal-Forecast-Models
原文摘要 · Abstract (English)
Modern IoT deployments for environmental sensing produce high volume spatiotemporal data to support downstream tasks such as forecasting, typically powered by machine learning models. While existing filtering and strategic deployment techniques optimize collected data volume at the edge, they overlook how variations in sampling frequencies and spatial coverage affect downstream model performance. In many forecasting models, incorporating data from additional sensors denoise predictions by providing broader spatial contexts. This interplay between sampling frequency, spatial coverage and different forecasting model architectures remain underexplored. This work presents a systematic study of forecasting models - classical models (VAR), neural networks (GRU, Transformer), spatio-temporal graph neural networks (STGNNs), and time series foundation models (TSFMs: Chronos Moirai, TimesFM) under varying spatial sensor nodes density and sampling intervals using real-world temperature data in a wireless sensor network. Our results show that STGNNs are effective when sensor deployments are sparse and sampling rate is moderate, leveraging spatial correlations via encoded graph structure to compensate for limited coverage. In contrast, TSFMs perform competitively at high frequencies but degrade when spatial coverage from neighboring sensors is reduced. Crucially, the multivariate TSFM Moirai outperforms all models by natively learning cross-sensor dependencies. These findings offer actionable insights for building efficient forecasting pipelines in spatio-temporal systems. All code for model configurations, training, dataset, and logs are open-sourced for reproducibility: https://github.com/UIUC-MONET-Projects/Benchmarking-Spatiotemporal-Forecast-Models
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