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 paper 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.

翻译：时间序列分类与外部回归是重要且具有挑战性的机器学习任务。深度学习已推动自然语言处理和计算机视觉领域的革命性进展，并在时间序列分析等其他领域展现出巨大潜力——因为在这些领域，相关特征往往需要从原始数据中抽象提取，且无法预先获知。本文针对深度学习在时间序列分类与外部回归这一快速发展领域中的当前前沿技术进行综述。我们系统梳理了用于这些任务的不同网络架构与训练方法，并探讨了将深度学习应用于时间序列数据时面临的挑战与机遇。此外，我们还总结了时间序列分类与外部回归的两个关键应用场景：人体活动识别与卫星对地观测。