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Petrel: Personalized Trend Line Estimation with Limited Labels from One Individual

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Advances in Knowledge Discovery and Data Mining (PAKDD 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13938))

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Abstract

This study proposes a framework for generating customized trend lines that consider user preferences and input time series shapes. The existing trend estimators fail to capture individual needs and application domain requirements. The proposed framework obtains users’ preferred trends by asking users to draw trend lines on sample datasets. The experiments and case studies demonstrate the effectiveness of the model. Code and dataset are available at https://github.com/Anthony860810/Generating-Personalized-Trend-Line-Based-on-Few-Labelings-from-One-Individual.

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Acknowledgements

This work is partially supported by the National Science and Technology Council of Taiwan under grant 110-2222-E-008-005-MY3.

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Authors and Affiliations

  1. National Central University, Taoyuan, Taiwan

    Tong-Yi Kuo & Hung-Hsuan Chen

Authors
  1. Tong-Yi Kuo
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  2. Hung-Hsuan Chen
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Correspondence to Hung-Hsuan Chen .

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Editors and Affiliations

  1. Kyoto University, Kyoto, Japan

    Hisashi Kashima

  2. IBM Research, Thomas J. Watson Research Center, Yorktown Heights, NY, USA

    Tsuyoshi Ide

  3. National Chiao Tung University, Hsinchu, Taiwan

    Wen-Chih Peng

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Kuo, TY., Chen, HH. (2023). Petrel: Personalized Trend Line Estimation with Limited Labels from One Individual. In: Kashima, H., Ide, T., Peng, WC. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2023. Lecture Notes in Computer Science(), vol 13938. Springer, Cham. https://doi.org/10.1007/978-3-031-33383-5_22

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  • DOI: https://doi.org/10.1007/978-3-031-33383-5_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-33382-8

  • Online ISBN: 978-3-031-33383-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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