Feature Selection for Multivariate Time Series via Network Pruning
This addresses the problem of feature selection in multivariate time series for domains like wearable devices, but it is incremental as it builds on existing deep learning approaches.
The paper tackles the challenge of high dimensionality in multivariate time series data by proposing a Neural Feature Selector (NFS) as an end-to-end solution for feature selection, achieving comparable results with state-of-the-art methods on four real-world datasets.
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 Selector (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 (a stream corresponds to an univariate series of MTS) 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.