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SSDTW: Shape segment dynamic time warping

Authors
Hong, Jae YeolPark, Seung HwanBaek, Jun-Geol
Issue Date
15-7월-2020
Publisher
PERGAMON-ELSEVIER SCIENCE LTD
Keywords
Time series data; Dynamic time warping (DTW); Shape segment dynamic time warping (SSDTW); Maximal overlap discrete wavelet transform (MODWT); Alignment path
Citation
EXPERT SYSTEMS WITH APPLICATIONS, v.150
Indexed
SCIE
SCOPUS
Journal Title
EXPERT SYSTEMS WITH APPLICATIONS
Volume
150
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/54392
DOI
10.1016/j.eswa.2020.113291
ISSN
0957-4174
Abstract
In order to increase the yield of a process, it is essential to establish a process control based on manufacturing data. Process management systems mainly consist of statistical process control (SPC), fault detection and classification (FDC), and advanced process control (APC), and are modeled using time series data. However, large amounts of time series data and various distributions are collected in the process; hence, preprocessing measures, such as length adjustment, are essential for modeling. Dynamic time warping (DTW) has been widely used as an algorithm that can measure the similarity between two different time series data and adjust their length. However, owing to the complex structure and time lag of processing time series data, there are limitations in applying the traditional DTW. Therefore, to solve this problem, we propose the shape segment dynamic time warping (SSDTW) algorithm that improves DTW in consideration of the structure information of time series data. By using the maximum overlap discrete wavelet transform (MODWT), the proposed method reflects the peripheral information of the time series data and divides the time series data interval to achieve a reasonable local alignment path. SSDTW attains more accurate alignment paths than DTW, derivative dynamic time warping (DDTW), and shapeDTW. Experiments conducted using semiconductor signal data and UCR time series data sets show that the proposed method is more effective than DTW, DDTW, and shapeDTW. (C) 2020 Elsevier Ltd. All rights reserved.
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공과대학 (산업경영공학부)
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