论文标题
通过数据驱动的节点子采样在网络时间序列中的图形上选择传感器
Sensor selection on graphs via data-driven node sub-sampling in network time series
论文作者
论文摘要
本文涉及到在时间序列网络上选择最佳传感器采样集的问题，以便在最小重建误差的非观察传感器下进行信号恢复。该问题是由在冗余网络上收集时间依赖的图形信号的应用程序的动机。在这种情况下，人们可能只希望使用一部分传感器来预测基础图中节点的整个集合中的数据流。一个典型的应用是可以减少电池供应有限的传感器网络中的功耗。我们建议并比较各种数据驱动的策略，以关闭固定数量的传感器或等效地选择一个节点采样集。我们还将我们的方法与现有文献有关传感器选择的方法与（可能是）基础图结构相关联。我们的方法结合了多元时间序列分析，图形信号处理，高维度和深度学习中的统计学习的工具。为了说明我们方法的性能，我们报告了有关不同城市自行车共享网络的真实数据分析的数值实验。
This paper is concerned by the problem of selecting an optimal sampling set of sensors over a network of time series for the purpose of signal recovery at non-observed sensors with a minimal reconstruction error. The problem is motivated by applications where time-dependent graph signals are collected over redundant networks. In this setting, one may wish to only use a subset of sensors to predict data streams over the whole collection of nodes in the underlying graph. A typical application is the possibility to reduce the power consumption in a network of sensors that may have limited battery supplies. We propose and compare various data-driven strategies to turn off a fixed number of sensors or equivalently to select a sampling set of nodes. We also relate our approach to the existing literature on sensor selection from multivariate data with a (possibly) underlying graph structure. Our methodology combines tools from multivariate time series analysis, graph signal processing, statistical learning in high-dimension and deep learning. To illustrate the performances of our approach, we report numerical experiments on the analysis of real data from bike sharing networks in different cities.
