Data streaming classification has become an essential task in many fields where real-time decisions have to be made
based on incoming information. Neural networks are a particularly suitable technique for the streaming scenario due to their
incremental learning nature. However, the high computation cost of deep architectures limits their applicability to high-velocity
streams, hence they have not yet been fully explored in the literature. Therefore, in this work, we aim to evaluate the effectiveness
of complex deep neural networks for supervised classification in the streaming context. We propose an asynchronous deep
learning framework in which training and testing are performed simultaneously in two different processes. The data stream
entering the system is dual fed into both layers in order to concurrently provide quick predictions and update the deep learning
model. This separation reduces processing time while obtaining high accuracy on classification. Several time-series datasets
from the UCR repository have been simulated as streams to evaluate our proposal, which has been compared to other methods
such as Hoeffding trees, drift detectors, and ensemble models. The statistical analysis carried out verifies the improvement in
performance achieved with our dual-pipeline deep learning framework, that is also competitive in terms of computation time.Ministerio de Economía y Competitividad TIN2017-88209-C2-2-
