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

翻译：现代物联网环境感知部署产生海量时空数据，以支持预测等下游任务，这些任务通常由机器学习模型驱动。尽管现有的过滤与策略性部署技术优化了边缘端的数据采集量，但它们忽略了采样频率与空间覆盖范围的变化如何影响下游模型性能。在许多预测模型中，通过纳入更多传感器的数据以提供更广泛的空间上下文，能够降低预测噪声。采样频率、空间覆盖范围与不同预测模型架构之间的相互作用仍未得到充分探索。本研究基于无线传感器网络中的真实温度数据，系统性地研究了在空间传感器节点密度与采样间隔变化条件下，经典模型（VAR）、神经网络（GRU、Transformer）、时空图神经网络（STGNNs）以及时间序列基础模型（TSFMs：Chronos、Moirai、TimesFM）等预测模型的表现。结果表明：当传感器部署稀疏且采样率适中时，STGNNs通过编码图结构利用空间相关性以弥补覆盖范围的限制，表现优异；相反，TSFMs在高采样频率下具有竞争力，但当邻近传感器的空间覆盖减少时性能下降。关键在于，多元TSFM模型Moirai通过原生学习传感器间依赖关系，在所有模型中表现最佳。这些发现为构建时空系统中高效的预测流程提供了可操作的见解。所有模型配置、训练代码、数据集及日志均已开源以确保可复现性：https://github.com/UIUC-MONET-Projects/Benchmarking-Spatiotemporal-Forecast-Models