Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.
SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland.
Methods Mol Biol. 2021;2190:33-71. doi: 10.1007/978-1-0716-0826-5_2.
With the biomedical field generating large quantities of time series data, there has been a growing interest in developing and refining machine learning methods that allow its mining and exploitation. Classification is one of the most important and challenging machine learning tasks related to time series. Many biomedical phenomena, such as the brain's activity or blood pressure, change over time. The objective of this chapter is to provide a gentle introduction to time series classification. In the first part we describe the characteristics of time series data and challenges in its analysis. The second part provides an overview of common machine learning methods used for time series classification. A real-world use case, the early recognition of sepsis, demonstrates the applicability of the methods discussed.
随着生物医学领域产生大量的时间序列数据，人们越来越感兴趣于开发和完善机器学习方法，以挖掘和利用这些数据。分类是与时间序列相关的最重要和最具挑战性的机器学习任务之一。许多生物医学现象，如大脑活动或血压，随时间而变化。本章的目的是提供时间序列分类的一个简单介绍。在第一部分中，我们描述了时间序列数据的特征和在其分析中面临的挑战。第二部分概述了常用于时间序列分类的常见机器学习方法。一个实际的应用案例，即败血症的早期识别，演示了所讨论方法的适用性。
