  Processing data streams arriving at high speed requires the development of
models that can provide fast and accurate predictions. Although deep neural
networks are the state-of-the-art for many machine learning tasks, their
performance in real-time data streaming scenarios is a research area that has
not yet been fully addressed. Nevertheless, there have been recent efforts to
adapt complex deep learning models for streaming tasks by reducing their
processing rate. The design of the asynchronous dual-pipeline deep learning
framework allows to predict over incoming instances and update the model
simultaneously using two separate layers. The aim of this work is to assess the
performance of different types of deep architectures for data streaming
classification using this framework. We evaluate models such as multi-layer
perceptrons, recurrent, convolutional and temporal convolutional neural
networks over several time-series datasets that are simulated as streams. The
obtained results indicate that convolutional architectures achieve a higher
performance in terms of accuracy and efficiency.
