华东师范大学学报(自然科学版) ›› 2021, Vol. 2021 ›› Issue (5): 115-133.doi: 10.3969/j.issn.1000-5641.2021.05.011

• 数据分析与应用 • 上一篇    下一篇

早期时间序列分类方法研究综述

杨梦晨, 陈旭栋, 蔡鹏, 倪葎*()   

  1. 华东师范大学 数据科学与工程学院, 上海 200062
  • 收稿日期:2021-08-03 出版日期:2021-09-25 发布日期:2021-09-28
  • 通讯作者: 倪葎 E-mail:lni@dase.ecnu.edu.cn

Survey of early time series classification methods

Mengchen YANG, Xudong CHEN, Peng CAI, Lyu NI*()   

  1. School of Data Science and Engineering, East China Normal University, Shanghai 200062, China
  • Received:2021-08-03 Online:2021-09-25 Published:2021-09-28
  • Contact: Lyu NI E-mail:lni@dase.ecnu.edu.cn

摘要:

传感器技术的普及使得时间序列数据受到人们越来越多的关注. 早期时间序列分类(Early Time Series Classification, ETSC)希望通过观测尽可能短的时序数据而对其做出尽可能准确的分类, 已在科技金融领域发挥着重要的作用. 首先概述了常见的时间序列分类器, 并综述了基于最小预测长度、基于最大区分子序列和基于模型的3类早期时间序列分类框架的最新研究进展. 然后在每类方法中, 分析了具有代表性的早期时间序列分类模型的关键技术及其优缺点; 整理了科技金融领域的公开数据集和常见的评价指标. 最后对未来的发展趋势做了展望.

关键词: 早期时间序列分类, 时间序列分类器, 最小预测长度, 最大区分子序列, 机器学习

Abstract:

With the increasing popularity of sensors, time-series data have attracted significant attention. Early time series classification (ETSC) aims to classify time-series data with the highest level of accuracy and smallest possible size. ETSC, in particular, plays a critical role in fintech. First, this paper summarizes the common classifiers for time-series data and reviews the current research progress on minimum prediction length-based, shapelet-based, and model-based ETSC frameworks. There are pivotal technologies, advantages, and disadvantages of the representative ETSC methods in separate frameworks. Next, we review public time-series datasets in fintech and commonly used performance evaluation criteria. Lastly, we explore future research directions pertinent to ETSC.

Key words: early time series classification, time series classifier, minimum prediction length, shapelet, machine learning

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