Journal of East China Normal University(Natural Science) ›› 2021, Vol. 2021 ›› Issue (5): 115-133.doi: 10.3969/j.issn.1000-5641.2021.05.011

• Data Analysis and Applications • Previous Articles     Next Articles

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


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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