Time Series Classification and Extrinsic Regression are important and challenging machine learning tasks. Deep learning has revolutionized natural language processing and computer vision and holds great promise in other fields such as time series analysis where the relevant features must often be abstracted from the raw data but are not known a priori. This article surveys the current state of the art in the fast-moving field of deep learning for time series classification and extrinsic regression. We review different network architectures and training methods used for these tasks and discuss the challenges and opportunities when applying deep learning to time series data. We also summarize two critical applications of time series classification and extrinsic regression, human activity recognition and satellite earth observation.

时间序列分类和外在回归是重要且具有挑战性的机器学习任务。深度学习彻底改变了自然语言处理和计算机视觉，并在时间序列分析等其他领域具有广阔的前景，在这些领域中，相关特征通常必须从原始数据中抽象出来，但事先并不知道。本文调查了快速发展的时间序列分类和外在回归深度学习领域的当前技术水平。我们回顾了用于这些任务的不同网络架构和训练方法，并讨论了将深度学习应用于时间序列数据时的挑战和机遇。我们还总结了时间序列分类和外在回归、人类活动识别和卫星地球观测的两个关键应用。