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.
Bibliographical noteFunding Information:
This research was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) ( NRF-2019R1A2C2005949 ). This research was also supported by Korea Institute for Advancement of Technology (KIAT) grant funded by the Korea Government (MOTIE) ( P0008691 , The Competency Development Program for Industry Specialist).
- Alignment path
- Dynamic time warping (DTW)
- Maximal overlap discrete wavelet transform (MODWT)
- Shape segment dynamic time warping (SSDTW)
- Time series data
ASJC Scopus subject areas
- General Engineering
- Computer Science Applications
- Artificial Intelligence