马超红,翁小清.基于PAA的时间序列早期分类[J].计算机科学,2018,45(2):291-296, 317
基于PAA的时间序列早期分类
Early Classification of Time Series Based on Piecewise Aggregate Approximation
投稿时间:2016-12-02  修订日期:2017-02-27
DOI:10.11896/j.issn.1002-137X.2018.02.050
中文关键词:  时间序列,早期分类,维数约简,分段聚合近似
英文关键词:Time series,Early classification,Dimensionality reduction,Piecewise aggregate approximation
基金项目:
作者单位E-mail
马超红 河北经贸大学信息技术学院 石家庄050061 chaohma@126.com 
翁小清 河北经贸大学信息技术学院 石家庄050061 xqweng@126.com 
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中文摘要:
      在时间序列数据挖掘领域,时间序列的早期分类越来越受到人们的重视,由于时间序列的长度(也称为维数)较大,在早期分类的实际应用中选择合适的维数约简方法非常重要,因此提出一种基于分段聚合近似(PAA)的时间序列早期分类方法。首先运用PAA对时间序列样本进行维数约简,然后在低维空间对样本进行早期分类,在43个时间序列数据集上的实验结果表明, 所提方法 在准确率、早期性、可靠性等方面优于已有方法。
英文摘要:
      Early classification on time series is more and more significant in the field of time series data ming.As the high dimension of time series data,it is of highly necessary to choose an efficient and appreciate dimensionality reduction method in the practical application of early classification on time series.Thus,this paper aimed at applying piecewise aggregate approximation to time series data,and then implemented early classification in lower dimension.In addition,through making comparison with some existing methods,the experiments were carried on in forty-three datasets.The experimental result indicates that this proposal is better than other existing methods in accuracy,earliness and reliability.
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