深度学习
计算机科学
人工智能
时间序列
系列(地层学)
机器学习
人工神经网络
数据挖掘
生物
古生物学
作者
Bryan Lim,Stefan Zohren
标识
DOI:10.1098/rsta.2020.0209
摘要
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.
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