计算机工程与应用2017，Vol.53Issue(24)：64-68,5.DOI:10.3778/j.issn.1002-8331.1611-0153
基于数学形态学的时间序列相似性度量研究
Mathematical morphology based time series similarity measure
摘要
Abstract
Similarity measure is a keystone in mining time series data. Numerous methods have been proposed to deal with it over the past decades. This paper summarizes the mainstream similarity measure algorithms so far, points out the defects within each of them. A newly mathematical morphology based similarity measure method is proposed to over-come the low discriminated precision problem. The core part of the mentioned method is dilation and erosion operation, which can strengthen the noise-resistance performance while keeping the difference among different time series at the same time, improving the precision of measurement. The experiment is tested on 8 dataset using KNN classification as evaluation metrics, which turns out that the proposed method improves at most 20%in classification precision, compared with DTW algorithm.关键词
数学形态学/时间序列/相似性度量Key words
mathematical morphology/time series/similarity measure分类
信息技术与安全科学引用本文复制引用
臧艺超,邱菡,周天阳,朱俊虎..基于数学形态学的时间序列相似性度量研究[J].计算机工程与应用,2017,53(24):64-68,5.基金项目
国家自然科学基金(No.61502528). （No.61502528）
