Time series forecasting has become a very intensive field of research, which is even increasing in recent years. Deep neural networks have proved to be powerful and are achieving high accuracy in many application fields. For these reasons, they are one of the most widely used methods of machine learning to solve problems dealing with big data nowadays. In this work, the time series forecasting problem is initially formulated along with its mathematical fundamentals. Then, the most common deep learning architectures that are currently being successfully applied to predict time series are described, highlighting their advantages and limitations. Particular attention is given to feed forward networks, recurrent neural networks (including Elman, long-short term memory, gated recurrent units, and bidirectional networks), and convolutional neural networks. Practical aspects, such as the setting of values for hyper-parameters and the choice of the most suitable frameworks, for the successful application of deep learning to time series are also provided and discussed. Several fruitful research fields in which the architectures analyzed have obtained a good performance are reviewed. As a result, research gaps have been identified in the literature for several domains of application, thus expecting to inspire new and better forms of knowledge.

中文翻译：

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时间序列预测的深度学习：一项调查

时间序列预测已经成为一个非常密集的研究领域，近年来甚至还在增加。深度神经网络已被证明是强大的，并在许多应用领域实现了高精度。由于这些原因，它们是当今解决大数据处理问题的最广泛使用的机器学习方法之一。在这项工作中，时间序列预测问题最初是与其数学基础一起制定的。然后，描述了目前成功应用于预测时间序列的最常见的深度学习架构，强调了它们的优点和局限性。特别关注前馈网络、循环神经网络（包括 Elman、长短期记忆、门控循环单元和双向网络），和卷积神经网络。还提供并讨论了实际方面，例如超参数值的设置和最合适框架的选择，以便将深度学习成功应用于时间序列。回顾了几个富有成效的研究领域，其中所分析的体系结构获得了良好的性能。因此，在文献中已经确定了几个应用领域的研究空白，从而期望激发新的和更好的知识形式。