TY - GEN
T1 - Modeling Transitions of Inter-segment Patterns for Time Series Representation
AU - Sun, I. Fu
AU - Ting, Lo Pang Yun
AU - Su, Ko Wei
AU - Chuang, Kun Ta
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
PY - 2024
Y1 - 2024
N2 - Against the backdrop of technological advancements, we are now equipped to collect and analyze time series data in unparalleled ways, offering significant value across various fields. However, traditional time series data analysis often leans heavily on expert insight. This study introduces a novel approach to time series data analysis based on the shapelet evolution graph, designed to intuitively capture core patterns and characteristics within the data without the need for expert intervention. Comparative analysis reveals that our approach excels in scenarios with explicit pattern transitions. Our research not only offers a fresh perspective and methodology for time series data analysis, through comparison with other baseline methods, but also provides foundational knowledge to predict whether a dataset exhibits pattern transition phenomena.
AB - Against the backdrop of technological advancements, we are now equipped to collect and analyze time series data in unparalleled ways, offering significant value across various fields. However, traditional time series data analysis often leans heavily on expert insight. This study introduces a novel approach to time series data analysis based on the shapelet evolution graph, designed to intuitively capture core patterns and characteristics within the data without the need for expert intervention. Comparative analysis reveals that our approach excels in scenarios with explicit pattern transitions. Our research not only offers a fresh perspective and methodology for time series data analysis, through comparison with other baseline methods, but also provides foundational knowledge to predict whether a dataset exhibits pattern transition phenomena.
UR - http://www.scopus.com/inward/record.url?scp=85190799129&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85190799129&partnerID=8YFLogxK
U2 - 10.1007/978-981-97-1711-8_5
DO - 10.1007/978-981-97-1711-8_5
M3 - Conference contribution
AN - SCOPUS:85190799129
SN - 9789819717101
T3 - Communications in Computer and Information Science
SP - 61
EP - 74
BT - Technologies and Applications of Artificial Intelligence - 28th International Conference, TAAI 2023, Proceedings
A2 - Lee, Chao-Yang
A2 - Lin, Chun-Li
A2 - Chang, Hsuan-Ting
PB - Springer Science and Business Media Deutschland GmbH
T2 - 28th International Conference on Technologies and Applications of Artificial Intelligence, TAAI 2023
Y2 - 1 December 2023 through 2 December 2023
ER -