  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.
