In recent years, there has been an ever increasing amount of multivariate time series (MTS) data in various domains, typically generated by a large family of sensors such as wearable devices. This has led to the development of novel learning methods on MTS data, with deep learning models dominating the most recent advancements. Prior literature has primarily focused on designing new network architectures for modeling temporal dependencies within MTS. However, a less studied challenge is associated with high dimensionality of MTS data. In this paper, we propose a novel neural component, namely Neural Feature Se-lector (NFS), as an end-2-end solution for feature selection in MTS data. Specifically, NFS is based on decomposed convolution design and includes two modules: firstly each feature stream within MTS is processed by a temporal CNN independently; then an aggregating CNN combines the processed streams to produce input for other downstream networks. We evaluated the proposed NFS model on four real-world MTS datasets and found that it achieves comparable results with state-of-the-art methods while providing the benefit of feature selection. Our paper also highlights the robustness and effectiveness of feature selection with NFS compared to using recent autoencoder-based methods.

翻译：近年来,不同领域多变时间序列(MTS)数据的数量在不断增多,通常由磨损装置等大型传感器组成的大型组合生成。这导致在MTS数据方面开发了创新的学习方法,而深层次的学习模式主导了最近的进步。以前的文献主要侧重于设计新的网络结构,以模拟MTS内的时间依赖。然而,研究较少的挑战与MTS数据的高度维度有关。在本文件中,我们提出了一个新颖的神经元组成部分,即神经地貌感应器(NFS),作为MTS数据特征选择的端端端2的解决方案。具体地说,NFS基于分解的演进设计,包括两个模块:首先,MTFS内部的每个特征流由CN独立处理;然后是CNN综合的CNN将经过加工的流组合起来,以便为其他下游网络提供投入。我们评估了四个真实世界的MTS数据集的拟议NFS模式,发现它在提供地貌选择的惠益的同时,取得了与状态-艺术方法的可比的结果。我们的文件还强调了最近采用与自动选择特征的方法的稳健和效力。