Por mais que problemas de reconhecimento de imagens, ou mesmo de segmentação sonora estejam em alta em Deep Learning, 90% dos problemas do mundo quando falamos de dados, passam por dados estruturados, em especial séries temporais. Esse paper mostra uma metodologia pouco convencional (a transformação de séries temporais em uma ‘imagem’ para o uso de uma Rede Coevolucionária) mas que pode mostrar que o céu é o limite quando falamos de arranjos para solução de problemas de predição usando dados estruturados.
Abstract: In many real-world application, e.g., speech recognition or sleep stage classification, data are captured over the course of time, constituting a Time-Series. Time-Series often contain temporal dependencies that cause two otherwise identical points of time to belong to different classes or predict different behavior. This characteristic generally increases the difficulty of analysing them. Existing techniques often depended on hand-crafted features that were expensive to create and required expert knowledge of the field. With the advent of Deep Learning new models of unsupervised learning of features for Time-series analysis and forecast have been developed. Such new developments are the topic of this paper: a review of the main Deep Learning techniques is presented, and some applications on Time-Series analysis are summaried. The results make it clear that Deep Learning has a lot to contribute to the field.
Conclusions: When applying Deep Learning, one seeks to stack several independent neural network layers that, working together, produce better results than the already existing shallow structures. In this paper, we have reviewed some of these modules, as well the recent work that has been done by using them, found in the literature. Additionally, we have discussed some of the main tasks normally performed when manipulating Time-Series data using deep neural network structures. Finally, a more specific focus was given on one work performing each one of these tasks. Employing Deep Learning to Time-Series analysis has yielded results in these cases that are better than the previously existing techniques, which is an evidence that this is a promising field for improvement.