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Modeling Transitions of Inter-segment Patterns for Time Series Representation

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Technologies and Applications of Artificial Intelligence (TAAI 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2074))

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Abstract

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.

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Acknowledgement

This paper was supported in part by Ministry of Science and Technology, R.O.C., under Contract 112-2221-E-006-158 and 1122622-8-006-010-TD1.

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Correspondence to Kun-Ta Chuang .

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Sun, IF., Ting, L.PY., Su, KW., Chuang, KT. (2024). Modeling Transitions of Inter-segment Patterns for Time Series Representation. In: Lee, CY., Lin, CL., Chang, HT. (eds) Technologies and Applications of Artificial Intelligence. TAAI 2023. Communications in Computer and Information Science, vol 2074. Springer, Singapore. https://doi.org/10.1007/978-981-97-1711-8_5

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  • DOI: https://doi.org/10.1007/978-981-97-1711-8_5

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-1710-1

  • Online ISBN: 978-981-97-1711-8

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