[1]LI Hailin,LIANG Ye.Dynamic time warping based on piecewise aggregate approximation and data derivatives[J].CAAI Transactions on Intelligent Systems,2016,11(2):249-256.[doi:10.11992/tis.201507064]
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Dynamic time warping based on piecewise aggregate approximation and data derivatives

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 11 Number of periods: 2016 2 Page number: 249-256 Column: 学术论文—人工智能基础 Public date: 2016-04-25
Title:
Dynamic time warping based on piecewise aggregate approximation and data derivatives
Author(s):
LI Hailin;  LIANG Ye
Department of Information Management, Huaqiao University, Quanzhou 362021, China
Keywords:
dynamic time warping; time series; piecewise aggregate approximation; numerical derivative; similarity measure; classification; dimensionality reduction; feature representation
CLC:
TP301
DOI:
10.11992/tis.201507064
Abstract:
Dynamic time warping (DTW) is often used to measure the similarity of time series data; however, it has efficiency and quality limitations. In this study, a novel DTW method combining piecewise aggregate approximation (PAA) and derivatives is proposed to measure the similarity of time series. The dimensionality of the time series data was effectively reduced by PAA, and the feature sequence was transformed into new sequences by combining the numerical derivatives after the dimensionality reduction. Furthermore, the DTW design corresponded to the similarity measurement method of the feature sequence. The experimental results demonstrate that the proposed method is superior because it has better measurement quality, obtains a better classification effect in time series data mining, and has high efficiency in lower dimensional spaces.
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