中北大学学报（自然科学版）2024，Vol.45Issue(4)：420-427,480,9.DOI:10.3969/j.issn.1673-3193.2024.04.002
一种基于趋势距离的快速Shapelet提取算法
A Fast Shapelet Extraction Algorithm Based on Trend Distance
摘要
Abstract
Aiming at the problem that the existing Shapelet extraction method cannot reflect the trend characteristics and the extraction result deviates slightly from the original data,an improved fast Shapelet selection algorithm was proposed.A distance calculation method considering the relative trend of time series was proposed,which could measure the similarity of time series more accurately.Secondly,the Shapelet features were combined with the ensemble network to enable the classifier to benefit from the residual linear connection and attention mechanism,which enhanced the generalization ability of the algo-rithm.Finally,controlled trials were conducted on 12 datasets.Experimental results show that the pro-posed method can obtain an average accuracy of 88.0%,which is 2.9%higher than the fast Shapelet algorithm,especially on the ChlorineConcentration dataset,and the accuracy is increased by 13.3%.In terms of acceleration rate,the method can extract faster than the original algorithm on all 10 datasets,so it can extract Shapelet in time series data more efficiently.关键词
Shapelet/趋势特征/Shapelet变换/子类划分/时间序列分类Key words
Shapelet/trend characteristics/Shapelet transform/subclass division/time series classification分类
信息技术与安全科学引用本文复制引用
张苗苗,乔钢柱,李泽宇..一种基于趋势距离的快速Shapelet提取算法[J].中北大学学报（自然科学版）,2024,45(4):420-427,480,9.基金项目
山西省基础研究计划联合资助项目(TZLH20230818007) （TZLH20230818007）
