关键词: 数学形态学, 时间序列, 相似性度量

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 overcome 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.

Key words: mathematical morphology, time series, similarity measure