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효과적인 시계열 데이터 분류를 위한 동적시간왜곡 기반의 시계열 길이 변환

Title
효과적인 시계열 데이터 분류를 위한 동적시간왜곡 기반의 시계열 길이 변환
Other Titles
Transformation of Variant-Length Time Series based on Dynamic Time Warping for Effective Classification
Author
허선
Keywords
Time Series Classification; Sensor Data; Dynamic Time Warping; Variant-Length Time Series
Issue Date
2020-08
Publisher
대한산업공학회
Citation
대한산업공학회지, v. 46, No. 4, Page. 356-364
Abstract
Time series data produced from various sensors are mostly different from each other in length due to various environment of collection. Most time series classification methods, however, assume that the lengths of time series data are the same. There is no alternative, moreover, for variant-length time series classification except DTW and DTW-related methods and few researches on transformation of time series length can be found in direct ways. In this paper, we propose a length transformation method for effective variant-length time series classification. Proposed method is a similarity-preserving transformation and the restoration is possible. To evaluate the proposed method, experiments are conducted to compare the classification performance by applying well-known methods to the time series data before and after the transformation. The classification using the dataset transformed by the proposed method shows better performance on almost all measurements.
URI
https://www.dbpia.co.kr/journal/articleDetail?nodeId=NODE09415225https://repository.hanyang.ac.kr/handle/20.500.11754/164675
ISSN
1225-0988; 2234-6457
DOI
10.7232/JKIIE.2020.46.4.356
Appears in Collections:
COLLEGE OF ENGINEERING SCIENCES[E](공학대학) > INDUSTRIAL AND MANAGEMENT ENGINEERING(산업경영공학과) > Articles
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