A new methodology is proposed for the analysis, modeling and forecasting of data collected from a wireless sensor network. Our approach is considered in the framework of a functional data analysis paradigm where observed data is represented in a functional form. To reduce dimensionality, functional principal components analysis is applied to highlight important underlying characteristics and find patterns of variations. The principal scores are modeled with tensor product smooths that allow for smoothing over space and time. The model is then used for simultaneous spatial prediction at unsampled locations and to forecast future observations. We consider soil temperature data from a wireless sensor network of 50 sensor nodes in two different land types (grassland and forest) observed during 60 consecutive days in private property close to Yass, New South Wales, Australia.

中文翻译：

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无线传感器网络数据的时空函数数据分析

提出了一种用于分析、建模和预测从无线传感器网络收集的数据的新方法。我们的方法是在功能数据分析范式的框架中考虑的，其中观察到的数据以功能形式表示。为了降低维度，应用功能主成分分析来突出重要的潜在特征并找到变化模式。主要分数用张量积平滑建模，允许在空间和时间上进行平滑。然后，该模型用于在未采样位置同时进行空间预测并预测未来的观测。我们考虑了来自两个不同土地类型（草地和森林）的 50 个传感器节点的无线传感器网络的土壤温度数据，该网络在亚斯附近的私人财产中连续 60 天观察到，