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：现代物联网环境传感部署产生高容量时空数据，以支持预测等下游任务，通常由机器学习模型驱动。虽然现有的滤波和策略性部署技术优化了边缘端收集的数据量，但它们忽略了采样频率和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，通过纳入来自额外传感器的数据提供更广泛的空间上下文，从而对预测进行去噪。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。本研究利用无线传感器网络中的真实温度数据，系统性地研究了预测模型——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）——在不同空间传感器节点密度和采样间隔下的表现。我们的结果表明，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来补偿有限的覆盖范围，表现出色。相比之下，TSFMs 在高频率下具有竞争力，但当来自邻近传感器的空间覆盖范围减少时性能下降。关键的是，多元 TSFM Moirai 通过原生学习跨传感器依赖关系，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行的见解。所有模型配置、训练、数据集和日志的代码均已开源以确保可复现性：https://github.com/UIUC-MONET-Projects/Benchmarking-Spatiotemporal-Forecast-Models