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

Tong Yi Kuo; Hung Hsuan Chen

doi:10.1007/978-3-031-33383-5_22

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

Tong Yi Kuo, Hung Hsuan Chen

Department of Computer Science and Information Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	English
Title of host publication	Advances in Knowledge Discovery and Data Mining - 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023, Proceedings
Editors	Hisashi Kashima, Tsuyoshi Ide, Wen-Chih Peng
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	276-288
Number of pages	13
ISBN (Print)	9783031333828
DOIs	https://doi.org/10.1007/978-3-031-33383-5_22
State	Published - 2023
Event	27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023 - Osaka, Japan Duration: 25 May 2023 → 28 May 2023

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13938 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023
Country/Territory	Japan
City	Osaka
Period	25/05/23 → 28/05/23

Keywords

Time series analysis
Trend estimation

Access to Document

10.1007/978-3-031-33383-5_22

深度學習的基礎技術革新：針對最佳化方式、網路結構、及模型複雜度(2/3)
Chen, H.-H. (PI)
1/08/22 → 31/07/23
Project: Research

Cite this

Kuo, T. Y., & Chen, H. H. (2023). Petrel: Personalized Trend Line Estimation with Limited Labels from One Individual. In H. Kashima, T. Ide, & W.-C. Peng (Eds.), Advances in Knowledge Discovery and Data Mining - 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023, Proceedings (pp. 276-288). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13938 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-33383-5_22

Kuo, Tong Yi ; Chen, Hung Hsuan. / Petrel : Personalized Trend Line Estimation with Limited Labels from One Individual. Advances in Knowledge Discovery and Data Mining - 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023, Proceedings. editor / Hisashi Kashima ; Tsuyoshi Ide ; Wen-Chih Peng. Springer Science and Business Media Deutschland GmbH, 2023. pp. 276-288 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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{\textquoteright} 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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Kuo, TY & Chen, HH 2023, Petrel: Personalized Trend Line Estimation with Limited Labels from One Individual. in H Kashima, T Ide & W-C Peng (eds), Advances in Knowledge Discovery and Data Mining - 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13938 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 276-288, 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023, Osaka, Japan, 25/05/23. https://doi.org/10.1007/978-3-031-33383-5_22

Petrel: Personalized Trend Line Estimation with Limited Labels from One Individual. / Kuo, Tong Yi; Chen, Hung Hsuan.
Advances in Knowledge Discovery and Data Mining - 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023, Proceedings. ed. / Hisashi Kashima; Tsuyoshi Ide; Wen-Chih Peng. Springer Science and Business Media Deutschland GmbH, 2023. p. 276-288 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13938 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - 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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Kuo TY, Chen HH. Petrel: Personalized Trend Line Estimation with Limited Labels from One Individual. In Kashima H, Ide T, Peng WC, editors, Advances in Knowledge Discovery and Data Mining - 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023, Proceedings. Springer Science and Business Media Deutschland GmbH. 2023. p. 276-288. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-33383-5_22