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

Tong Yi Kuo, Hung Hsuan Chen

研究成果: 書貢獻/報告類型會議論文篇章同行評審

摘要

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.

原文???core.languages.en_GB???
主出版物標題Advances in Knowledge Discovery and Data Mining - 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023, Proceedings
編輯Hisashi Kashima, Tsuyoshi Ide, Wen-Chih Peng
發行者Springer Science and Business Media Deutschland GmbH
頁面276-288
頁數13
ISBN(列印)9783031333828
DOIs
出版狀態已出版 - 2023
事件27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023 - Osaka, Japan
持續時間: 25 5月 202328 5月 2023

出版系列

名字Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
13938 LNCS
ISSN(列印)0302-9743
ISSN(電子)1611-3349

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???event.eventtypes.event.conference???27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023
國家/地區Japan
城市Osaka
期間25/05/2328/05/23

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