论文标题
深度学习的时间序列预测：调查
Time Series Forecasting With Deep Learning: A Survey
论文作者
论文摘要
已经开发了许多深度学习体系结构，以适应不同领域的时间序列数据集的多样性。在本文中，我们调查了在一步和多野体时间序列预测中使用的常见编码器和解码器设计，描述了如何通过每个模型将时间信息纳入预测中。接下来，我们重点介绍了混合深度学习模型的最新发展，该模型将良好的统计模型与神经网络组件相结合，以改善这两种类别的纯方法。最后，我们概述了深度学习还可以通过时间序列数据促进决策支持的一些方式。
Numerous deep learning architectures have been developed to accommodate the diversity of time series datasets across different domains. In this article, we survey common encoder and decoder designs used in both one-step-ahead and multi-horizon time series forecasting -- describing how temporal information is incorporated into predictions by each model. Next, we highlight recent developments in hybrid deep learning models, which combine well-studied statistical models with neural network components to improve pure methods in either category. Lastly, we outline some ways in which deep learning can also facilitate decision support with time series data.
