标题:使用图神经网络处理变维时间序列

摘要】物联网(IoT)技术的几种应用涉及从多个传感器捕获数据,从而产生多传感器时间序列。用于这种多传感器或多元时间序列建模的基于神经网络的现有方法假定输入尺寸或传感器数量固定。这样的方法在实际环境中可能会遇到困难,在这种情况下,同一设备或设备(例如手机,可穿戴设备,引擎等)的不同实例带有已安装传感器的不同组合。我们考虑从此类多传感器时间序列中训练神经网络模型,其中,由于在每个时间序列的来源或传感器的不同子集的可用性或安装,时间序列的输入维数有所变化。我们提出了一种适用于零脉冲传输学习的新颖神经网络架构,该功能允许对多变量时间序列进行可靠的推断,而先前在测试时看不见可用尺寸或传感器的组合。通过用“条件向量”对基于核心神经网络的时间序列模型的各层进行调节来实现这种组合概括,该“条件矢量”携带每个时间序列的可用传感器组合信息。通过经由图神经网络汇总与时间序列中的可用传感器相对应的学习的“传感器嵌入矢量”的集合来获得该调节矢量。我们在公开可用的活动识别和设备预测数据集上评估了所提出的方法,并表明与深度门控递归神经网络基线相比,所提出的方法可以更好地概括。

Title: Handling Variable-Dimensional Time Series with Graph Neural Networks

[abstract]  Several applications of Internet of Things(IoT) technology involve capturing data from multiple sensors resulting in multi-sensor time series. Existing neural networks based approaches for such multi-sensor or multivariate time series modeling assume fixed input dimension or number of sensors. Such approaches can struggle in the practical setting where different instances of the same device or equipment such as mobiles, wearables, engines, etc. come with different combinations of installed sensors. We consider training neural network models from such multi-sensor time series, where the time series have varying input dimensionality owing to availability or installation of a different subset of sensors at each source of time series. We propose a novel neural network architecture suitable for zero-shot transfer learning allowing robust inference for multivariate time series with previously unseen combination of available dimensions or sensors at test time. Such a combinatorial generalization is achieved by conditioning the layers of a core neural network-based time series model with a “conditioning vector” that carries information of the available combination of sensors for each time series. This conditioning vector is obtained by summarizing the set of learned “sensor embedding vectors” corresponding to the available sensors in a time series via a graph neural network. We evaluate the proposed approach on publicly available activity recognition and equipment prognostics datasets, and show that the proposed approach allows for better generalization in comparison to a deep gated recurrent neural network baseline.

【作者】Vibhor Gupta, Jyoti Narwariya, Pankaj Malhotra, Lovekesh Vig, Gautam Shroff

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