Journal of East China Normal University(Natural Science) >
Survey of early time series classification methods
Received date: 2021-08-03
Online published: 2021-09-28
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.
Mengchen YANG , Xudong CHEN , Peng CAI , Lyu NI . Survey of early time series classification methods[J]. Journal of East China Normal University(Natural Science), 2021 , 2021(5) : 115 -133 . DOI: 10.3969/j.issn.1000-5641.2021.05.011
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中国综合性科技类核心期刊(北大核心)