Selecting Features from Time Series Using Attention-Based Recurrent Neural Networks

Capturing, storing, and analyzing high-dimensional time series data are important challenges that need to be effectively tackled nowadays, as the extremely large amounts of such data are being generated every second. In this paper, we introduce the recurrent neural networks equipped with attention modules that quantify the importance of features, hence can be employed to select only an informative subset of all available features. Additionally, our models are trained in an end-to-end fashion, hence are directly applicable to infer over the unseen data. Our experiments included datasets from various domains and showed that the proposed technique is data-driven, easily applicable to new use cases, and competitive to other dimensionality reduction algorithms.